CORAL can be used in scientific studies.

Here you can find the list of articles where the CORAL software has been used or cited.

If you wish to correct the list or if you have questions, please contact us.

Examples of applications of the CORAL for research works,

which were carried out without participation of developers from Mario Negri Institute:

2011-2024

2024

Gupta, S., Kashyap, M., Bansal, Y., & Bansal, G.
In silico insights into design of novel VEGFR-2 inhibitors: SMILES-based QSAR modelling, and docking studies on substituted benzo-fused heteronuclear derivatives.
SAR and QSAR in Environmental Research,(2024) 1–20. https://doi.org/10.1080/1062936X.2024.2332203

D. Petković, M. Deljanin Ilić, D. Simonović, Z. Marcetić, M. Stojanović, S. Stojanović, N. Arsić, D. Sokolović, A. Veselinović,
QSAR Modeling of Sphingomyelin Synthase 2 Inhibitors for Their Potential as Anti-Atherosclerotic Agents.
Acta Chim. Slov.2024,71, 170–178. DOI: 10.17344/acsi.2023.8566

Padhy, I., Banerjee, B., Achary, P.G.R., Gupta, P.P., Sharma, T.,
Design, synthesis, 2D-QSAR, molecular dynamic simulation, and biological evaluation of topiramate–phenolic acid conjugates as PPARγ inhibitors.
Futur J Pharm Sci 10, 44 (2024). https://doi.org/10.1186/s43094-024-00617-1

Ali Azimi, Shahin Ahmadi, Marjan Jebeli Javan, Morteza Rouhani, Zohreh Mirjafary,
QSAR models for the ozonation of diverse volatile organic compounds at different temperatures.
RSC Adv., 2024, 14, 8041-8052. https://doi.org/10.1039/D3RA08805G

Vukomanović, P., Stefanović, M., Stevanović, J.M., Petrić, A., Trenkić, M., Andrejević, L., Lazarević, M., Sokolović, D., Veselinović, A.M.
Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability.
Pharm Res (2024). https://doi.org/10.1007/s11095-024-03675-5

Shahin Ahmadi, Shahram Lotfi, Hamideh Hamzehali, Parvin Kumar,
A simple and reliable QSPR model for prediction of chromatography retention indices of volatile organic compounds in peppers.
RSC Adv., 2024, 14, 3186-3201. https://doi.org/10.1039/D3RA07960K

Bhawna, Sunil Kumar, Parvin Kumar, Ashwani Kumar,
Correlation Intensity Index-Index of Ideality of Correlation: A hyphenated target function for furtherance of MAO-B inhibitory activity assessment,
Computational Biology and Chemistry, 108, 2024, 107975, https://doi.org/10.1016/j.compbiolchem.2023.107975.

Jianbo Tong, Peng Gao, Haiyin Xu, Yuan Liu,
Improved SAR and QSAR models of SARS-CoV-2 Mpro inhibitors based on machine learning,
Journal of Molecular Liquids, 394, 2024, 123708, https://doi.org/10.1016/j.molliq.2023.123708

Mohamed Ouabane, Khadija Zaki, Kamal Tabti, Marwa Alaqarbeh, Abdelouahid Sbai, Chakib Sekkate, Mohammed Bouachrine, Tahar Lakhlifi,
Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations,
Computers in Biology and Medicine, 169 (2024) 107880. https://doi.org/10.1016/j.compbiomed.2023.107880

Abdelmoujoud Faris, Ivana Cacciatore, Ibrahim M. Ibrahim, Mohammed H. AL Mughram, Hanine Hadni, Kamal Tabti & Menana Elhallaoui
In silico computational drug discovery: a Monte Carlo approach for developing a novel JAK3 inhibitors,
Journal of Biomolecular Structure and Dynamics, Published online: 20 Oct 2023. DOI: 10.1080/07391102.2023.2270709

Rahul Singh, Parvin Kumar, Jayant Sindhu, Ashwani Kumar & Sohan Lal
CORAL: probing the structural requirements for a-amylase inhibition activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives based on QSAR with correlation intensity index, molecular docking, molecular dynamics, and ADMET studies,
Journal of Biomolecular Structure and Dynamics, Published online: 10 Oct 2023. DOI: 10.1080/07391102.2023.2265490

2023

A. Antović, R. Karadžić, J.V. Živković, and A.M. Veselinović,
Development of QSAR Model Based on Monte Carlo Optimization for Predicting GABAA Receptor Binding of Newly Emerging Benzodiazepines.
Acta Chim. Slov. Vol. 70 No. 4 (2023). https://doi.org/10.17344/acsi.2023.8465

Surbhi Goyal, Payal Rani, Monika Chahar, Khalid Hussain, Parvin Kumar, Jayant Sindhu,
Analysis of good and bad fingerprint for identification of NIR based optical frameworks using Monte Carlo method,
Microchemical Journal, 2023, 109549, https://doi.org/10.1016/j.microc.2023.109549

Shahram Lotfi, Shahin Ahmadi, Ali Azimib and Parvin Kumar,
Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique.
New J. Chem., 2023, 47, 19504-19515. https://doi.org/10.1039/D3NJ03696K

Živadinović, B., Stamenović, J., Živadinović, J., Živadinović, L., Živadinović, A., Stojanović, M., Lazarević, M., Sokolović, D., Veselinović, A.M.,
Monte Carlo optimization based QSAR modeling, molecular docking studies, and ADMET predictions of compounds with antiMES activity.
Struct. Chem. 34, pages 2225–2235, (2023). https://doi.org/10.1007/s11224-023-02238-5

Yanting Pang, Ruoyu Li, Ze Zhang, Jiali Ying, Menghan Li, Fuxian Li, Ting Zhang,
Based on the Nano-QSAR model: Prediction of factors influencing damage to C. elegans caused by metal oxide nanomaterials and validation of toxic effects,
Nano Today, 52, 2023, 101967. https://doi.org/10.1016/j.nantod.2023.101967

N. Nikolić, T. Kostić, M. Golubović, T. Nikolić, M.Marinković, V. Perić, S. Mladenović and A.M. Veselinović,
Monte Carlo Optimization Based QSAR Modeling of Angiotensin II Receptor Antagonists.
Acta Chim. Slov. 2023, 70, 318–326. DOI: 10.17344/acsi.2023.8081

Mohamed Ouabane, Kamal Tabti, Halima Hajji, Mhamed Elbouhi, Ayoub Khaldan, Khalid Elkamel, Abdelouahid Sbai, Mohammed Aziz Ajana, Chakib Sekkate, Mohammed Bouachrine, Tahar Lakhlifi,
Structure-odor relationship in pyrazines and derivatives: a physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, Molecular Docking, ADME-Tox and Molecular Dynamics,
Arabian Journal of Chemistry, Volume 16, Issue 11, 2023, 105207, https://doi.org/10.1016/j.arabjc.2023.105207.

Rezaie-keikhaie, N., Shiri, F., Ahmadi, S., Salahinejad, M.,
QSTR based on Monte Carlo approach using SMILES and graph features for toxicity toward Tetrahymena pyriformis.
Journal of the Iranian Chemical Society, Volume 20, pages 2609–2620, (2023). https://doi.org/10.1007/s13738-023-02859-x

Tajiani, F., Ahmadi, S., Lotfi, S., Kumar, P., Almasirad, A.,
In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization.
BMC Chemistry 17, 87 (2023). https://doi.org/10.1186/s13065-023-00999-y

Beilei Yuan, Yunlin Wang, Cheng Zong, Leqi Sang, Shuang Chen, Chengzhi Liu, Yong Pan, Huazhong Zhang,
Modeling study for predicting altered cellular activity induced by nanomaterials based on Dlk1-Dio3 gene expression and structural relationships,
Chemosphere, 335, 2023, 139090. https://doi.org/10.1016/j.chemosphere.2023.139090

K. Bagri, A. Kapoor, P. Kumar & A. Kumar,
Hybrid descriptors–conjoint indices: a case study on imidazole-thiourea containing glutaminyl cyclase inhibitors for design of novel anti-Alzheimer’s candidates,
SAR and QSAR in Environmental Research, 34:5, (2023) 361-381. DOI: 10.1080/1062936X.2023.2212175

Parvin Kumar, Ashwani Kumar, Jayant Sindhu, Sohan Lal,
Quasi-SMILES as a basis for the development of QSPR models to predict the CO₂ capture capacity of deep eutectic solvents using correlation intensity index and consensus modelling,
Fuel, 345, 2023, 128237, https://doi.org/10.1016/j.fuel.2023.128237

Oubahmane, M.; Hdoufane, I.; Delaite, C.; Sayede, A.; Cherqaoui, D.; El Allali, A.
Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.
Pharmaceuticals 2023, 16, 608. https://doi.org/10.3390/ph16040608

Soleymani, N., Ahmadi, S., Shiri, F., Almasirad, A.
QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors.
BMC Chemistry 17, 32 (2023). https://doi.org/10.1186/s13065-023-00947-w

Surbhi Goyal, Payal Rani, Monika Chahar, Khalid Hussain, Parvin Kumar & Jayant Sindhu,
Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies,
Journal of Biomolecular Structure and Dynamics, 41:23, 13616-13631, 2023. DOI: 10.1080/07391102.2023.2193991

Kamal Tabti, Oumayma Abdessadak, Abdelouahid Sbai, Hamid Maghat, Mohammed Bouachrine, Tahar Lakhlifi,
Design and development of novel spiro-oxindoles as potent antiproliferative agents using quantitative structure activity based Monte Carlo method, docking molecular, molecular dynamics, free energy calculations, and pharmacokinetics /toxicity studies.
Journal of Molecular Structure, 1284, 2023, 135404, https://doi.org/10.1016/j.molstruc.2023.135404

Thanh, Ly Cong,
Towards Developing Quantitative Structure-activity Relationship Models for the Design of Novel Influenza A Inhibitors Targeting Neuraminidase.
VNU Journal of Science: Medical and Pharmaceutical Sciences, [S.l.], v. 39, n. 1, 2023. ISSN 2588-1132. https://doi.org/10.25073/2588-1132/vnumps.4412

2022

Hamzehali, H., Lotfi, S., Ahmadi, S., Kumar, P., 
Quantitative structure–activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes.
Sci. Rep. 12, 21708 (2022). https://doi.org/10.1038/s41598-022-26279-8

Shahram Lotfi, Shahin Ahmadi and Parvin Kumar,
Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach.
RSC Adv., 2022, 12, 24988-24997. https://doi.org/10.1039/D2RA03936B

Ghasemi, G., Nasiri, N.
Using QSAR calculations on benzamide derivatives to inhibit reproduction in endothelial cells by CORAL SEA.
Pakistan Journal of Pharmaceutical Sciences, 35 (3),(2022) pp. 841-844. DOI: 10.36721/PJPS.2022.35.3.REG.841-844.1

Kamal Tabti, Larbi Elmchichi, Abdelouahid Sbai, Hamid Maghat, Mohammed Bouachrine & Tahar Lakhlifi.
Molecular modelling of antiproliferative inhibitors based on SMILES descriptors using Monte-Carlo method, docking, MD simulations and ADME/Tox studies,
Molecular Simulation, Published online: 12 Aug 2022. DOI: 10.1080/08927022.2022.2110246

B. Živadinović, J. Stamenović, J. Živadinović, L. Živadinović, M. Sokolović, S.S. Filipović, D. Sokolović, A.M. Veselinović,
QSAR Modelling, Molecular Docking studies and ADMET predictions of Polysubstituted Pyridinylimidazoles as Dual Inhibitors of JNK3 and p38a MAPK,
Journal of Molecular Structure, 1265, 2022, 133504, https://doi.org/10.1016/j.molstruc.2022.133504

Ðordević V, Petković M, Živković J, Nikolić GM, Veselinović AM.
Development of Novel Therapeutics for Schizophrenia Treatment Based on a Selective Positive Allosteric Modulation of a1-Containing GABAARs—In Silico Approach.
Current Issues in Molecular Biology. 2022; 44(8):3398-3412. https://doi.org/10.3390/cimb44080234

Parvin Kumar, Ashwani Kumar, Devender Singh,
CORAL: Development of A hybrid descriptor based QSTR model to predict the toxicity of Dioxins and Dioxin-like Compounds with Correlation Intensity Index and Consensus Modelling,
Environmental Toxicology and Pharmacology, 93, 2022, 103893, https://doi.org/10.1016/j.etap.2022.103893

Parvin Kumar, Ashwani Kumar, Sohan Lal, Devender Singh, Shahram Lotfi, Shahin Ahmadi,
CORAL: Quantitative Structure Retention Relationship (QSRR) of flavors and fragrances compounds studied on the stationary phase methyl silicone OV-101 column in gas chromatography using correlation intensity index and consensus modelling,
Journal of Molecular Structure, 1265, 2022, 133437. https://doi.org/10.1016/j.molstruc.2022.133437

Xingang Jia, Wenzhen Wang, Bo Yang, Chunbao Du,
Study of quantitative structure-property relationship for density of ionic liquids based on Monte Carlo optimization.
MATEC Web of Conferences 358, 01011 (2022). https://doi.org/10.1051/matecconf/202235801011

Bunmahotama, W., Vijver, M.G. and Peijnenburg, W.,
Development of a Quasi–Quantitative Structure–Activity Relationship Model for Prediction of the Immobilization Response of Daphnia magna Exposed to Metal-Based Nanomaterials.
Environmental Toxicology and Chemistry, Volume 41, Number 6, pp. 1439–1450, 2022. https://doi.org/10.1002/etc.5322

Atena Azimi, Shahin Ahmadi, Ashwani Kumar, Mahnaz Qomi & Ali Almasirad
SMILES-Based QSAR and Molecular Docking Study of Oseltamivir Derivatives as Influenza Inhibitors,
Polycyclic Aromatic Compounds, Published online: 23 Apr 2022. DOI: 10.1080/10406638.2022.2067194

Kumar, P., Kumar, S., & Kumar, A.
Creation of Quantitative Feature Toxicity Relationship Models for Cytotoxicity of Cadmium Containing Quantum Dots Towards HEK Cells Using QuasiSMILES.
International Journal of Quantitative Structure-Property Relationships (IJQSPR),(2022) 7(1), 1-20. http://doi.org/10.4018/IJQSPR.294900

Sayyadikord Abadi, R., Shojaei, A.F., Tatafei, F.E., Alizadeh O.,
Theoretical Study of Octreotide Derivatives as Anti-Cancer Drugs using QSAR, Monte Carlo Method and formation of Complexes.
Russ. J. Phys. Chem. B. 16, 127–137 (2022). https://doi.org/10.1134/S199079312201002X

Liman W, Oubahmane M, Hdoufane I, Bjij I, Villemin D, Daoud R, Cherqaoui D, El Allali A.
Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus.
Molecules. 2022; 27(9):2729. https://doi.org/10.3390/molecules27092729

Shahin Ahmadi, Sepideh Ketabi, Mahnaz Qomi,
CO2 uptake prediction of metal–organic frameworks using quasi-SMILES and Monte Carlo optimization.
New J. Chem., 2022, 46, 8827-8837. https://doi.org/10.1039/D2NJ00596D

Ničkčović, V.P., Nikolić, G.R., Nedeljković, B.M., Mitić, N., Filipović Danić, S., Mitić, J., Marčetić, Z., Sokolović, D., Veselinović, A.M.,
In silico approach for the development of novel antiviral compounds based on SARS-COV-2 protease inhibition.
Chem. Pap. 76, pp. 4393–4404 (2022). https://doi.org/10.1007/s11696-022-02170-8

A. Kumar, P. Kumar, D. Singh,
QSRR modelling for the investigation of gas chromatography retention indices of flavour and fragrance compounds on Carbowax 20M glass capillary column with the index of ideality of correlation and the consensus modelling,
Chemometrics and Intelligent Laboratory Systems 224, (2022), 104552. https://doi.org/10.1016/j.chemolab.2022.104552

Đordević, V., Pešić, S., Živković, J., Nikolić, G.M., Veselinović, A.M.,
"Development of novel antipsychotic agents by inhibiting dopamine transporter – in silico approach",
New J. Chem., 46, 2687-2696, 2022. https://doi.org/10.1039/D1NJ04759K

Xiao Ding, Dongwei Kang, Lin Sun, Peng Zhan, Xinyong Liu,
Combination of 2D and 3D-QSAR studies on DAPY and DANA derivatives as potent HIV-1 NNRTIs.
Journal of Molecular Structure 1249 (2022) 131603. https://doi.org/10.1016/j.molstruc.2021.131603

Shahin Ahmadi, Shahram Lotfi & Parvin Kumar,
Quantitative structure–toxicity relationship models for predication of toxicity of ionic liquids towards Leukemia rat cell line IPC-81 based on index of ideality of correlation,
Toxicology Mechanisms and Methods, (2022) 32:4, 302-312. DOI: 10.1080/15376516.2021.2000686

Shahin Ahmadi, Zohreh Moradi, Ashwani Kumar & Ali Almasirad.
SMILES-based QSAR and molecular docking study of xanthone derivatives as a-glucosidase inhibitors,
Journal of Receptors and Signal Transduction, 42(4), 2022, 361-372. https://doi.org/10.1080/10799893.2021.1957932

Amin, S.A., Ghosh, K., Singh, S., Qureshi, I.A., Jha, T., Gayen, S.,
Exploring naphthyl derivatives as SARS-CoV papain-like protease (PLpro) inhibitors and its implications in COVID-19 drug discovery.
Mol Divers., 26, pages 215–228 (2022). https://doi.org/10.1007/s11030-021-10198-3

Meenakshi Duhan, Jayant Sindhu, Parvin Kumar, Meena Devi, Rahul Singh, Ramesh Kumar, Sohan Lal, Ashwani Kumar, Sudhir Kumar & Khalid Hussain.
Quantitative structure activity relationship studies of novel hydrazone derivatives as a-amylase inhibitors with index of ideality of correlation,
Journal of Biomolecular Structure and Dynamics, 2022; 40:11, 4933-4953. DOI: 10.1080/07391102.2020.1863861

2021

Parvin Kumar & Ashwani Kumar,
Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling,
Nanotoxicology, 15:9, 1199-1214, 2021. DOI: 10.1080/17435390.2021.2008039

N. R. Das, P. G. R. Achary,
Prediction of pEC50(M) and molecular docking study for the selective inhibition of arachidonate 5-lipoxygenase.
Ukr. Biochem. J., 2021, Vol. 93, N 6, pp. 101-118. https://doi.org/10.15407/ubj93.06.101

Maria Nikkar, Robabeh Sayyadikord Abadi, Asghar Alizadehdakhel, Ghasem Ghasemi.
Monte Carlo Method and a Novel Modelling-Optimization Approach on QSAR Study of Doxazolidine Drugs and DNA-Binding.
Russ. J. Phys. Chem. B 15, S32–S41 (2021). https://doi.org/10.1134/S199079312109013X

Menezes, R.P.B., Sousa, N.F., de Morais e Silva, L., Scotti, L., Lopes, W.S. and Scotti, M.T.
The Use and Evolution of Web Tools for Aquatic Toxicology Studies.
In Chemometrics and Cheminformatics in Aquatic Toxicology, K. Roy (Ed.).(2021). https://doi.org/10.1002/9781119681397.ch24

S. Ahmadi, S. Lotfi, S. Afshari, P. Kumar & E. Ghasemi,
CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors,
SAR and QSAR in Environmental Research, 32:12, (2021) 1013-1031. DOI: 10.1080/1062936X.2021.2003429

Shahram Lotfi, Shahin Ahmadi, and Parvin Kumar,
The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors.
RSC Adv., 2021, 11, 33849-33857. https://doi.org/10.1039/D1RA06861J

Wenzhen Wang, Bo Yang and Xingang Jia,
Predicting the melting point of imidazole-based ionic liquids using QSPR model based on SMILES optimal descriptors.
IOP Conf. Ser.: Earth Environ. Sci. 2021, 859, 012084. doi:10.1088/1755-1315/859/1/012084

Meenakshi Duhan, Parvin Kumar, Jayant Sindhu, Rahul Singh, Meena Devi, Ashwani Kumar, Ramesh Kumar, Sohan Lal,
Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient a-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies,
Computers in Biology and Medicine, 138, 2021, 104876. https://doi.org/10.1016/j.compbiomed.2021.104876

A. Kumar & P. Kumar,
Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation,
SAR and QSAR in Environmental Research, 2021, 32:10, 817-834. https://doi.org/10.1080/1062936X.2021.1973095

Parvin Kumar, Ashwani Kumar,
Correlation Intensity Index (CII) as a benchmark of predictive potential: Construction of quantitative structure activity relationship models for anti-influenza single-stranded DNA aptamers using Monte Carlo optimization,
Journal of Molecular Structure, 1246, 2021, 131205, https://doi.org/10.1016/j.molstruc.2021.131205.

V. Perić, M. Golubović, M. Lazarević, V. Marjanović, T. Kostić, M. Đordević, D. Milić and A.M. Veselinović,
Development of potential therapeutics for pain treatment by inducing Sigma 1 receptor antagonism – in silico approach.
New J. Chem., 2021, 45, 12286-12295. https://doi.org/10.1039/D1NJ00883H

T. Ghiasi, S. Ahmadi, E. Ahmadi, M.R. Talei Bavil Olyai & Z. Khodadadi
The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors,
SAR and QSAR in Environmental Research, (2021) 32:6, 495-520, DOI: 10.1080/1062936X.2021.1925344

Das N.R., Achary P.G.R.
Quantitative Structure–Activity Relationships (QSARs) Study for KCNQ Genes (Kv7) and Drug Discovery.
In: Das S., Mohanty M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, (2021) vol 202.
Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_8

Shahram Lotfi, Shahin Ahmadi, Parvin Kumar,
A Hybrid Descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium Ionic Liquids using Monte Carlo approach,
Journal of Molecular Liquids, 2021, 116465. https://doi.org/10.1016/j.molliq.2021.116465

Golubović, M., Kostić, T., Djordjević, M., Perić, V., Lazarević, M., Milić, D.J., Marjanović, V., Veselinović, A.M.
In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7.
Computers in Biology and Medicine, 132, (2021) 104346. DOI: 10.1016/j.compbiomed.2021.104346

Arghya Banik, Kalyan Ghosh, Umesh K. Patil, Shovanlal Gayen,
Identification of molecular fingerprints of natural products for the inhibition of breast cancer resistance protein (BCRP).
Phytomedicine, 85, 2021, 153523. https://doi.org/10.1016/j.phymed.2021.153523

Ahmadi, S., Aghabeygi, S., Farahmandjou, M., Azimi,N .
The predictive model for band gap prediction of metal oxide nanoparticles based on quasi-SMILES.
Struct. Chem. 32, pages 1893–1905 (2021). https://doi.org/10.1007/s11224-021-01748-4

Nilima Rani Das, Sneha Prabha Mishra, P. Ganga Raju Achary,
Evaluation of Molecular Structure based Descriptors for the Prediction of pEC50(M) for the Selective Adenosine A_2A Receptor,
Journal of Molecular Structure, 2021, 130080, https://doi.org/10.1016/j.molstruc.2021.130080.

Nandi, S., Ghosh, K., Rathore, A., Sahu, A., & Gayen, S.
Monte Carlo Optimization-Based QSAR Study of Some Indole-Based Mcl-1 Inhibitors.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), 6(2), (2021) 1-18. doi:10.4018/IJQSPR.2021040101

Kalyan Ghosh, Sk. Abdul Amin, Shovanlal Gayen, Tarun Jha,
Chemical-informatics approach to COVID-19 drug discovery: Exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors.
Journal of Molecular Structure, 1224, 2021, 129026. https://doi.org/10.1016/j.molstruc.2020.129026

Ashwani Kumar, Parvin Kumar,
Cytotoxicity of quantum dots: Use of quasiSMILES in development of reliable models with index of ideality of correlation and the consensus modelling.
Journal of Hazardous Materials, 402, 2021, 123777. https://doi.org/10.1016/j.jhazmat.2020.123777

Kumar, A., Kumar, P.
Identification of good and bad fragments of tricyclic triazinone analogues as potential PKC-θ inhibitors through SMILES–based QSAR and molecular docking.
Struct. Chem. volume 32, pages 149–165 (2021). https://doi.org/10.1007/s11224-020-01629-2

Meenakshi Duhan, Rahul Singh, Meena Devi, Jayant Sindhu, Rimpy Bhatia, Ashwani Kumar & Parvin Kumar.
Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as a-amylase inhibitor,
Journal of Biomolecular Structure and Dynamics, 2021, 39:1, 91-107. DOI: 10.1080/07391102.2019.1704885

Qi, R., Pan, Y., Cao, J., Yuan, B., Wang, Y., Jiang, J.
Toward comprehension of the cytotoxicity of heterogeneous TiO₂-based engineered nanoparticles: a nano-QSAR approach
Environmental Science: Nano, 8 (4), 2021, pp. 927-936. DOI: 10.1039/d0en01266a

Sk. Abdul Amin, Kalyan Ghosh, Shovanlal Gayen & Tarun Jha,
Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors,
Journal of Biomolecular Structure and Dynamics, 39:13, 4764-4773, 2021. DOI: 10.1080/07391102.2020.1780946

Ahmadi, S., Ghanbari, H., Lotfi, S., Azimi, N.,
Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method.
Mol. Divers. 2021, 25(1), pp. 87–97. https://doi.org/10.1007/s11030-019-10026-9

Ashwani Kumar, Jayant Sindhu & Parvin Kumar,
In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N-myristoyltransferase using Monte Carlo method with index of ideality of correlation,
Journal of Biomolecular Structure and Dynamics, 2021, 39:14, 5014-5025. DOI: 10.1080/07391102.2020.1784286

2020

Duchowicz, P.R., Aranda, J.F., Bacelo, D.E., Fioressi, S.E.
QSPR study of the Henry's law constant for heterogeneous compounds
Chemical Engineering Research and Design, 154,(2020) pp. 115-121.
https://www.sciencedirect.com/science/article/abs/pii/S0263876219305763?via%3Dihub

C. Rojas, J.F. Aranda, E. Pacheco Jaramillo, I. Losilla, P. Tripaldi, P.R. Duchowicz, E.A. Castro,
Foodinformatic prediction of the retention time of pesticide residues detected in fruits and vegetables using UHPLC/ESI Q-Orbitrap,
Food Chemistry, 2020, 128354, https://doi.org/10.1016/j.foodchem.2020.128354

M. Javidfar & S. Ahmadi,
QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation,
SAR and QSAR in Environmental Research, (2020) 31:10, 717-739.

P. Kumar & A. Kumar,
In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation,
SAR and QSAR in Environmental Research, (2020) 31:9, 697-715.

Chopdar, Kumar Sambhav; Dash, Ganesh Chandra; Mohapatra, Pranab Kishor; Nayak, Binata; Raval, Mukesh Kumar,
Monte-Carlo Method Based QSAR Model to Discover Phytochemical Urease Inhibitors Using SMILES and GRAPH Descriptors.
ChemRxiv. Preprint.(2020): https://doi.org/10.26434/chemrxiv.12948587.v1

Ashwani Kumar, Kiran Bagri, Manisha Nimbhal & Parvin Kumar,
In silico exploration of the fingerprints triggering modulation of glutaminyl cyclase inhibition for the treatment of Alzheimer’s disease using SMILES based attributes in Monte Carlo optimization,
Journal of Biomolecular Structure and Dynamics, 2020 Aug, 14: 1-13. DOI: 10.1080/07391102.2020.1806111

Ashwani Kumar, Parvin Kumar,
Quantitative structure toxicity analysis of ionic liquids toward acetylcholinesterase enzymes using novel QSTR models with index of ideality of correlation and correlation contradictions index.
Journal of Molecular Liquids, Volume 318, 2020, 114055. https://doi.org/10.1016/j.molliq.2020.114055

Lotfi, S., Ahmadi, S. & Zohrabi, P.
QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants.
Struct Chem 31, 2257–2270 (2020). https://doi.org/10.1007/s11224-020-01568-y

Maja Zivkovic, Marko Zlatanovic, Nevena Zlatanovic, Mladjan Golubović, Aleksandar M. Veselinović,
The application of the combination of Monte Carlo optimization method based QSAR modeling and molecular docking in drug design and development.
Mini-Reviews in Medicinal Chemistry, 2020, 20(14), 1389-1402. DOI: 10.2174/1389557520666200212111428

Kumar, A., Kumar, P.
Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in Monte Carlo optimization.
Arch. Toxicol., 94, pages 3069–3086 (2020). https://doi.org/10.1007/s00204-020-02828-w

K. Ghosh, B. Bhardwaj, S.A. Amin, T. Jha & S. Gayen,
Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis,
SAR and QSAR in Environmental Research, 31:6, 439-455, 2020. DOI: 10.1080/1062936X.2020.1771769

Kiran Bagri, Ashwani Kumar, Manisha Nimbhal & Parvin Kumar.
Index of ideality of correlation and correlation contradiction index: a confluent perusal on acetylcholinesterase inhibitors,
Molecular Simulation, 46:10, 777-786, 2020. DOI: 10.1080/08927022.2020.1770753

Jiakai Cao,Yong Pan, Yanting Jiang, Ronghua Qi, Beilei Yuan, Zhenhua Jia, Juncheng Jiang, and Qingsheng Wang,
Computer-aided Nanotoxicology: Risk Assessment of Metal Oxide Nanoparticles via nano-QSAR.
Green Chem., 2020, 22, 3512-3521. https://doi.org/10.1039/D0GC00933D

M. Zivkovic, M. Zlatanovic, N. Zlatanovic, J. Djordjevic Jocic, M. Golubović, A.M. Veselinović,
Development of novel therapeutics for the treatment of glaucoma based on actin-binding kinase inhibition – in silico approach.
New J. Chem., 2020, 44, 6923-6931.

Ronghua Qi, Yong Pan, Jiakai Cao, Zhenhua Jia, Juncheng Jiang,
The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR.
Chemosphere, 249, 2020, 126175. https://doi.org/10.1016/j.chemosphere.2020.126175

Parvin Kumar, Ashwani Kumar,
CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index.
Chemometrics and Intelligent Laboratory Systems, 200, 2020, 103982. https://doi.org/10.1016/j.chemolab.2020.103982

Anurag T.K. Baidya, Kalyan Ghosh, Sk. Abdul Amin, Nilanjan Adhikari, Nirmal J, Tarun Jha and Shovanlal Gayen,
In silico modelling, identification of crucial molecular fingerprints, and prediction of new possible substrates of human organic cationic transporters 1 and 2 .
New J. Chem., 2020, 44, 4129-4143. https://doi.org/10.1039/C9NJ05825G

Jafari, K. & Fatemi, M.H.
Application of nano-quantitative structure–property relationship paradigm to develop predictive models for thermal conductivity of metal oxide-based ethylene glycol nanofluids.
J. Therm. Anal. Calorim. 142, pages 1335–1344 (2020). https://doi.org/10.1007/s10973-019-09215-3

Shahin Ahmadi,
Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria.
Chemosphere, 242, 2020, 125192. https://doi.org/10.1016/j.chemosphere.2019.125192

Dipayan Mondal, Kalyan Ghosh, Anurag T. K. Baidya, Anindia Mondal Gantait & Shovanlal Gayen,
Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of Nitroaromatics,
Toxicology Mechanisms and Methods, 30:4, 257-265, 2020. DOI: 10.1080/15376516.2019.1709238

Parvin Kumar & Ashwani Kumar.
Nucleobase Sequence Based Building up of Reliable QSAR Models with The Index of Ideality Correlation using Monte Carlo Method.
Journal of Biomolecular Structure and Dynamics, 38:11, 3296-3306, 2020. DOI: 10.1080/07391102.2019.1656109

Tomislav Kostić, Marina Deljanin Ilić, Zoran Perišić, Dragan Milić, Miodrag Đorđević, Mladjan Golubović, Goran Koraćević, Sonja Šalinger Martinović, Snežana Ćirić Zdravković, Saša Živić, Milan Lazarević, Dragana Stanojević, Sonja Dakić, Jelena Lilić & Aleksandar Veselinović.
Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - in silico approach,
Journal of Biomolecular Structure and Dynamics, 38:8, 2304-2313, 2020. DOI: 10.1080/07391102.2019.1630319

Vanja P. Ničković, Nebojša R. Mitić, Biljana D. Krdžić, Jelena D. Krdžić, Gordana R. Nikolić, Maja Z. Vasić, Goran Ranković, Petar Babović, Dušan Sokolović & Aleksandar M. Veselinović
Design and development of novel therapeutics for brucellosis treatment based on carbonic anhydrase inhibition,
Journal of Biomolecular Structure and Dynamics, Volume 38, Issue 6, 2020, Pages 1848-1857. DOI: 10.1080/07391102.2019.1619626

Nimbhal, M., Bagri, K., Kumar, P., Kumar, A.,
The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators.
Struct. Chem. 31, pages 831–839 (2020). https://doi.org/10.1007/s11224-019-01468-w

Sanskar Jain, Bhagwati Bhardwaj, Sk. Abdul Amin, Nilanjan Adhikari, Tarun Jha & Shovanlal Gayen,
Exploration of good and bad structural fingerprints for inhibition of Indoleamine-2,3-dioxygenase enzyme in cancer immunotherapy using Monte Carlo optimization and Bayesian classification QSAR modeling,
Journal of Biomolecular Structure and Dynamics, 38:6, 1683-1696, 2020. DOI: 10.1080/07391102.2019.1615000

Sanskar Jain, Sk. Abdul Amin, Nilanjan Adhikari, Tarun Jha & Shovanlal Gayen,
Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study,
Journal of Biomolecular Structure and Dynamics, 2020, 38:1, 66-77. DOI: 10.1080/07391102.2019.1566093

2019

Buglak A.A., Zherdev A.V., Dzantiev B.B. Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials. Molecules. 2019; 24(24): 4537. DOI:10.3390/molecules24244537

Silvina E. Fioressi, Daniel E. Bacelo, Pablo R. Duchowicz, QSAR study of human epidermal growth factor receptor (EGFR) inhibitors: conformation-independent models. Medicinal Chemistry Research, 2019, Volume 28, Issue 11, pp 2079–2087. https://doi.org/10.1007/s00044-019-02437-y

P. Kumar, A. Kumar & J. Sindhu. In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method, SAR and QSAR in Environmental Research, 2019, 30:8, 525-541. DOI: 10.1080/1062936X.2019.1629998

Zivkovic, M., Zlatanovic, M., Zlatanovic, N., Golubović, M., Veselinović, A.M. Development of novel therapeutics for glaucoma filtration surgery based on transforming growth factor-ß receptor 1 inhibition. New J. Chem., 2019,43, 19265-19273. http://dx.doi.org/10.1039/C9NJ05393J

Ghasem Ghasemi, A QSAR Study on the Biological Activities of Polyamines as Anti-alzheimer Drugs by Monte Carlo Optimization. Journal of Scientific & Industrial Research, Vol. 78, 2019, pp. 323-327. http://nopr.niscair.res.in/handle/123456789/47149

Khalid Bouhedjar, Abdelmalek Khorief Nacereddine, Hamida Ghorab, Abdelhafid Djerourou, QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors. International Journal of Quantitative Structure-Property Relationships (IJQSPR), 4(4), 2019, pages 15-26. DOI: 10.4018/IJQSPR.2019100102

S. Ahmadi, M. Mehrabi, S. Rezaei, N. Mardafkan, Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals. Journal of Molecular Structure, 1191, 2019, 165-174. https://doi.org/10.1016/j.molstruc.2019.04.103

Manisha, S. Chauhan, P. Kumar & A. Kumar (2019) Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method, SAR and QSAR in Environmental Research, 30:3, 145-159, DOI: 10.1080/1062936X.2019.1568299

P. Kumar, A. Kumar & J. Sindhu, (2019) Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR, SAR and QSAR in Environmental Research, 30:2, 63-80, DOI: 10.1080/1062936X.2018.1564067

Floresta, G.; Amata, E.; Gentile, D.; Romeo, G.; Marrazzo, A.; Pittalà, V.; Salerno, L.; Rescifina, A., Fourfold Filtered Statistical/Computational Approach for the Identification of Imidazole Compounds as HO-1 Inhibitors from Natural Products. Mar. Drugs 2019, 17, 113.

Ahmadi, S., Mardinia, F., Azimi, N., Qomi, M., Balali, E. Prediction of chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell by Monte Carlo method. Journal of Molecular Structure, 1181,(2019) 305-311.

S.Ć. Zdravković, M. Pavlović, S. Apostlović, G. Koracević, S.Š. Martinović, D. Stanojević, D. Sokolović, A.M. Veselinović, Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition - in silico approach. Computational Biology and Chemistry, 2019; 79: 55-62.

S.E. Fioressi, D.E.Bacelo, C. Rojas, J.F. Aranda, P.R.Duchowicz, Conformation-independent quantitative structure-property relationships study on water solubility of pesticides. Ecotoxicology and Environmental Safety, 171, 2019, 47-53.

Jang-Sik Choi, Tung X. Trinh, Tae-Hyun Yoon, Jongwoon Kim, Hyung-Gi Byun, Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. Chemosphere 217 (2019) 243-249.

Kumar P., Kumar A., Sindhu J., Lal S., QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method. Drug Res. (Stuttg), 2019; 69(3): 159-167. doi: 10.1055/a-0652-5290

Sonam Bhargava, Tarun Patel, Ruchi Gaikwad, Umesh Kumar Patil & Shovanlal Gayen, Identification of structural requirements and prediction of inhibitory activity of natural flavonoids against Zika virus through molecular docking and Monte Carlo based QSAR Simulation. Natural Product Research 33:6, 851-857, 2019. https://doi.org/10.1080/14786419.2017.1413574

Vanja P. Ničković, Zorica N. Vujnović-Živković, Rada Trajković, Dane Krtinić, Lidija Ristić, Milan Radović, Zorica Ćirić, Dušan Sokolović & Aleksandar M. Veselinović, In silico studies and the design of novel agents for the treatment of systemic tuberculosis, Journal of Biomolecular Structure and Dynamics, 37:12, (2019), 3198-3205.

2018

A.K. Halder, Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification. SAR and QSAR in Environmental Research, (2018) 29:11, 911-933.

S. Ahmadi & A. Akbari, Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method, SAR and QSAR in Environmental Research,(2018) 29:11, 895-909.

Floresta, G.; Amata, E.; Barbaraci, C.; Gentile, D.; Turnaturi, R.; Marrazzo, A.; Rescifina, A., A Structure- and Ligand-Based Virtual Screening of a Database of "Small" Marine Natural Products for the Identification of "Blue" Sigma-2 Receptor Ligands. Mar. Drugs 2018, 16 (10), 384.

Md Lutful Islam and Gulabchand K. Gupta. Application of Monte Carlo Algorithm to Explore Simplified Molecular-Input Line-Entry System based Molecular Descriptors of BACE1 inhibitors for Therapeutic Application in Alzheimer's Disease. International Journal of Computer Applications 182(11): 40-47, 2018.

V. Stoičkov, S. Šarić, M. Golubović, D. Zlatanović, D. Krtinić, L. Dinić, B. Mladenović, D. Sokolović, A.M. Veselinović, Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach. SAR and QSAR in Environmental Research, 29(7), 2018, 503-515.

Ashwani Kumar, Shilpi Chauhan, Use of Simplified Molecular Input Line Entry System and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors. Future medicinal chemistry 10(13) (2018) 1603-1622.

R. Gaikwad, S. Ghorai, Sk. A. Amin, N. Adhikari, T. Patel, K. Das, T. Jha, S. Gayen. Monte Carlo based modelling approach for designing and predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cell line MCF7. Toxicology in Vitro 52 (2018) 23-32.

Golubović M., Lazarević M., Zlatanović D., Krtinić D., Stoičkov V., Mladenović B., Milić D.J., Sokolović D., Veselinović A.M. The anesthetic action of some polyhalogenated ethers - Monte Carlo method based QSAR study. Computational Biology and Chemistry, Volume 75, 2018, Pages 32-38.

Parvin Kumar, Ashwani Kumar, Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles. Drug Res (Stuttg) 2018; 68(04): 189-195.

Tung Xuan Trinh, Jang-Sik Choi, Hyunpyo Jeon, Hyung-Gi Byun, Tae-Hyun Yoon, and Jongwoon Kim, Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. Chemical Research in Toxicology 2018, 31 (3), 183-190.

S. Begum, P.G.R. Achary, Optimal descriptor based QSPR models for catalytic activity of propylene polymerization. International Journal of Quantitative Structure-Property Relationships (IJQSPR), Volume 3, Issue 2, 2018, pp 36-48.

Veselinović, J.B., Ðordević, V., Bogdanović, M., Morić, I., Veselinović, A.M., QSAR modeling of dihydrofolate reductase inhibitors as a therapeutic target for multiresistant bacteria. Struct. Chem. (2018) 29: 541-551.

M. Zdravković, A. Antović, J. B. Veselinović, D.Sokolović, A. M. Veselinović, QSPR in forensic analysis - The prediction of retention time of pesticide residues based on the Monte Carlo method. Talanta, 2018, 178, 656-662.

Duchowicz, P.R., Bacelo, D.E., Fioressi, S.E., Palermo, V., Ibezim, N.E., Romanelli, G.P. QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors. Medicinal Chemistry Research, (2018) 27: 420-428.

V. Stoičkov, D. Stojanović, I. Tasic, S. Šarić, D. Radenković, P. Babović, D. Sokolović, A. M. Veselinović, QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method. Struct. Chem. (2018) 29: 441-449.

2017

L. Simon, A. Imane, K. K. Srinivasan, L. Pathak, I. Daoud, In Silico Drug-Designing Studies on Flavanoids as Anticolon Cancer Agents: Pharmacophore Mapping, Molecular Docking, and Monte Carlo Method-Based QSAR Modeling. Interdiscip. Sci. Comput. Life Sci. (2017) 9:445-458.

Bhargava S., Adhikari N., Amin S.A., Das K., Gayen S., Jha T., Hydroxyethylamine derivatives as HIV-1 protease inhibitors: a predictive QSAR modelling study based on Monte Carlo optimization. SAR QSAR Environ Res. 2017, 28(12): 973-990.

Amata E., Marrazzo A., Dichiara M., Modica M.N., Salerno L., Prezzavento O., Nastasi G., Rescifina A., Romeo G., Pittalà V., Heme Oxygenase Database (HemeOxDB) and QSAR analysis of isoform 1 inhibitors. Chem. Med. Chem. 2017 Nov 22; 12(22):1873-1881.

Aranda, J.F., Bacelo, D.E., Leguizamón Aparicio, M.S., Ocsachoque, M.A., Castro, E.A., Duchowicz, P.R. Predicting the bioconcentration factor through a conformation-independent QSPR study. SAR and QSAR in Environmental Research, 28 (9), (2017) pp. 749-763.

E. Amata, A. Marrazzo, M. Dichiara, M. N. Modica, L. Salerno, O. Prezzavento, G. Nastasi, A. Rescifina, G. Romeo, V. Pittalà, Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors. Data in Brief 15 (2017) 281-299.

Amin S.A., Bhargava S., Adhikari N., Gayen S., Jha T., Exploring pyrazolo[3,4- d ]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: A predictive approach combining comparative validated multiple molecular modeling techniques. J. Biomol. Struct. Dyn. 2017, 36(3), 590-608.

A. Rescifina, G. Floresta, A. Marrazzo, C. Parenti, O. Prezzavento, G. Nastasi, M. Dichiara, E. Amata, Sigma-2 receptor ligands QSAR model dataset. Data in Brief 13 (2017) 514-535.

Kumar, A., Chauhan, S., QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method. (2017) Drug Research, 67 (3), pp. 156-162.

A. Rescifina, G. Floresta, A. Marrazzo, C. Parenti, O. Prezzavento, G. Nastasi, M. Dichiara, E. Amata, Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis. European Journal of Pharmaceutical Sciences, 2017 Aug 30; 106: 94-101.

Pablo R. Duchowicz, Silvina E. Fioressi, Eduardo Castro, Karolina Wróbel, Nnenna E. Ibezim, and Daniel E. Bacelo. Conformation-Independent QSAR Study on Human Epidermal Growth Factor Receptor-2 (HER2) Inhibitors. ChemistrySelect 2017, 2, 3725-3731.

A. Kumar, S. Chauhan, Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors. SAR and QSAR in Environmental Research, 2017, 28(3):179-197.

D. Sokolović, J. Ranković, V. Stanković, R. Stefanović, S. Karaleić, B. Meki?, V. Milenković, J. Kocić, A.M. Veselinović. QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method. Med. Chem. Res. April 2017, Volume 26, Issue 4, pp. 796 - 804.

Ashwani Kumar and Shilpi Chauhan. Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors. Arch. Pharm. Chem. Life Sci. 2017, 350, e1600268.

Heidari, A., Fatemi, M.H. A Theoretical Approach to Model and Predict the Adsorption Coefficients of Some Small Aromatic Molecules on Carbon Nanotube. (2017) Journal of the Chinese Chemical Society, 64 (3), pp. 289 - 295.

Halder A. K., Achintya S. and Jha T. Predictive Quantitative Structure Toxicity Relationship Study on Avian Toxicity of Some Diverse Agrochemical Pesticides by Monte Carlo Method: QSTR on Pesticides, International Journal of Quantitative Structure-Property Relationships (IJQSPR) 2017, 2(1), 19-34.

2016

Cassano A, Marchese Robinson RL, Palczewska A, Puzyn T, Gajewicz A, Tran L, Manganelli S, Cronin MT. Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials. Altern Lab Anim. 2016; 44 (6): 533-556.

D. Sokolović,D. Aleksić, V. Milenković, S. Karaleić, D. Mitić, J. Kocić, B. Mekić, J. B. Veselinović, A. M. Veselinović, QSAR modeling of bis-quinolinium and bis-isoquinolinium compounds as acetylcholine esterase inhibitors based on the Monte Carlo method - the implication for Myasthenia gravis treatment. Med. Chem. Res. 2016, Volume 25, Issue 12, pp 2989-2998.

J. F. Aranda, J. C. Garro Martinez, E. A. Castro, P. R. Duchowicz, Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds. Int. J. Mol. Sci. 2016, 17, 1247.

D. Sokolović, V. Stanković, D. Toskić, L. Lilić, G. Ranković, J. Ranković, G. Nedin-Ranković, A. M. Veselinović. Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis. Structural Chemistry, 2016, Volume 27, Issue 5, pp. 1511-1519.

Md Ataul Islam, Tahir S. Pillay, Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors, Chemometrics and Intelligent Laboratory Systems, Volume 153, 2016, Pages 67-74.

2015

Hanieh Malekzadeh, Mohammad Hossein Fatemi, Setareh Gorji. Novel application of the CORAL software to model Cellular Uptake of Magnetofluorescent Nanoparticles in Pancreatic Cancer Cells. 5th Iranian Biennial Chemometrics Seminar, 25-26 Nov 2015. https://www.ics.ir/Files/Content/media/12454_file.pdf

Seyedeh Mozhgan Behgozin, Mohammad Hosein Fatemi and Kobra Samghani, In silico prediction of cutaneous penetration rate of some chemicals from their molecular structural descriptors. 5th Iranian Biennial Chemometrics Seminar, 25-26 Nov 2015. https://www.ics.ir/Files/Content/media/12454_file.pdf

Afsane Heidari, Mohammad H. Fatemi, Modeling and prediction of adsorption behavior of nanotubes. 5th Iranian Biennial Chemometrics Seminar, 25-26 Nov 2015. https://www.ics.ir/Files/Content/media/12454_file.pdf

S.E. Fioressi, D.E. Bacelo, W.P. Cui, L.M. Saavedra, P.R. Duchowicz, QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors. SAR and QSAR in Environmental Research, (2015 Jun) 26(6): 499-506.

J. V. Zivković, N. V. Trutić, J. B. Veselinović, G. M. Nikolić, A. M. Veselinović, Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors. Computers in Biology and Medicine, (2015) 64: 276-282.

S. Begum, P. Ganga Raju Achary. Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1). SAR and QSAR in environmental research, (2015) 26(5): 343-361.

Abdolmohammad Ghaedi. Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors, Journal of Molecular Liquids 208 (2015): 269-279.

L. Quesada-Romero,K. Mena-Ulecia, M. Zuñiga, P. De-la-Torre, D. Rossi, W. Tiznado, S. Collina, J. Caballero. Optimal graph-based and Simplified Molecular Input Line Entry System-based descriptors for quantitative structure-activity relationship analysis of arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines as σ 1 receptor ligands. J. Chemometrics, 2015, 29: 13-20.

J. B. Veselinović, G. M. Nikolić, N. V. Trutić, J. V. Zivković, A. M. Veselinović, Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase. SAR and QSAR in environmental research, 2015; Jun 4: 1-12.

A. M. Veselinović, J. B. Veselinović, J. V. Zivković, G.M. Nikolić. Application of SMILES notation based optimal descriptors in drug discovery and design, Current Topics in Medicinal Chemistry, 2015, 15(18 ): 1768-1779.

Fatemi, M.H., Malekzadeh, H. CORAL: Predictions of retention indices of volatiles in cooking rice using representation of the molecular structure obtained by combination of SMILES and graph approaches. Journal of the Iranian Chemical Society, (2015) 12(3): 405-412.

Chanchal Mondal, Amit Kumar Halder, Nilanjan Adhikari, Achintya Saha, Krishna Das Saha, Shovanlal Gayen, Tarun Jha. Comparative validated molecular modeling of p53-HDM2 inhibitors as antiproliferative agents. European Journal of Medicinal Chemistry 90 (2015) 860-875.

2014

Qian Li, Xiao Ding, Hongzong Si, Hua Gao. QSAR model based on SMILES of inhibitory rate of 2, 3-diarylpropenoic acids on AKR1C3. (2014) Chemom. Intell. Lab. Syst., 139, 132-138.

F. Deng, S. Ma, M. Xie, X. Zhang, P. Li and H. Zhai. Study on the agonists for the human Toll-like receptor-8 by molecular modeling. (2014) Mol. BioSyst. 10, 2202.

Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Prachayasittikul, V. QSAR study of H1N1 neuraminidase inhibitors from influenza a virus. (2014) Letters in Drug Design and Discovery, 11 (4), pp. 420-427.

Achary, P.G.R. QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software. (2014) SAR and QSAR in Environmental Research, 25 (6), pp. 507-526.

Deng, F.-F., Xie, M.-H., Li, P.-Z., Tian, Y.-L., Zhang, X.-Y., Zhai, H.-L. Study on the antagonists for the orphan G protein-coupled receptor GPR55 by quantitative structure-activity relationship. (2014) Chemometrics and Intelligent Laboratory Systems, 131, pp. 51-60.

Quesada-Romero, L., Caballero, J. Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3\upbeta β. (2014) Molecular Diversity, 18 (1), pp. 149-159.

Achary, P.G.R. Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2. (2014) SAR and QSAR in Environmental Research, 25 (1), pp. 73-90.

2012

Ibezim, E., Duchowicz, P.R., Ortiz, E.V., Castro, E.A. QSAR on aryl-piperazine derivatives with activity on malaria. (2012) Chemometrics and Intelligent Laboratory Systems, 110 (1), pp. 81-88.

2011

Garro Martinez, J.C., Duchowicz, P.R., Estrada, M.R., Zamarbide, G.N., Castro, E.A. QSAR study and molecular design of open-chain enaminones as anticonvulsant agents. (2011) International Journal of Molecular Sciences, 12 (12), pp. 9354-9368.

Garcia, J., Duchowicz, P.R., Rozas, M.F., Caram, J.A., Mirifico, M.V., Fernandez, F.M., Castro, E.A. A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases. (2011) Journal of Molecular Graphics and Modelling, 31, pp. 10-19.

Mullen, L.M.A., Duchowicz, P.R., Castro, E.A. QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents. (2011) Chemometrics and Intelligent Laboratory Systems, 107 (2), pp. 269-275.

• TO THE BEGINNING

The chronological list of publications which used or cited the CORAL:

2024

Toropova, A. P., Toropov, A. A., Roncaglioni, A., & Benfenati, E.
Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity towards tadpoles?
Toxicology Mechanisms and Methods,(2024), 1–9. https://doi.org/10.1080/15376516.2024.2332617

Alla P. Toropova and Andrey A. Toropov
The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES.
Science of the Total Environment 927 (2024) 172119. https://doi.org/10.1016/j.scitotenv.2024.172119

Wenjing Xie, Ziyi Xiong, Huimin Wang, Xiaoyi Liu, Hongyan Cui, Qiongyi Huang and Ying Tang,
The nanosafety assessment of ENMs under a dermal exposure scenario: from key molecular events to in silico modeling tools.
Environ. Sci.: Nano, 2024, 11, 708-738. http://dx.doi.org/10.1039/D3EN00585B

Alla P. Toropova, Andrey A. Toropov, Ivan Raska Jr., Maria Raskova, Ramon Carbό-Dorca,
The prediction of retention time of pesticide based on the Monte Carlo method with use the vector of ideality of correlation and correlation weights of local symmetry fragments.
Journal of Mathematical Chemistry, Accepted September 4, 2023. https://doi.org/10.1007/s10910-023-01517-0

A.P. Toropova, J. Meneses, E. Alfaro-Moreno, A.A. Toropov,
The system of self-consistent models based on quasi-SMILES as a tool to predict the potential of Nano-inhibitors of human lung carcinoma cell line A549 for different experimental conditions
Drug and Chemical Toxicology, Accepted Oct 12, 2022. https://doi.org/10.1080/01480545.2023.2174986

• TO THE BEGINNING

2023

Toropov, A.A.; Toropova, A.P.; Roncaglioni, A.; Benfenati, E.
Semi-Correlations for Building Up a Simulation of Eye Irritation.
Toxics 2023, 11(12), 993; https://doi.org/10.3390/toxics11120993

Toropov, A.A.; Toropova, A.P.; Roncaglioni, A.; Benfenati, E.; Leszczynska, D.; Leszczynski, J.
The System of Self-Consistent Models: The Case of Henry’s Law Constants.
Molecules 2023, 28, 7231. https://doi.org/10.3390/molecules28207231

Pengyu Chen, Yuxi Hu, Geng Chen, Na Zhao, Zhichao Dou,
Probing the bioconcentration and metabolism disruption of bisphenol A and its analogues in adult female zebrafish from integrated AutoQSAR and metabolomics studies,
Science of The Total Environment, Volume 905, 2023, 167011, https://doi.org/10.1016/j.scitotenv.2023.167011.

N. Fjodorova, M. Novic, K. Venko, B. Rasulev, M.T. Saçan, G. Tugcu, S.S. Erdem, A.P. Toropova, A.A. Toropov,
Cheminformatic and Machine Learning Approaches to the Assessment of Aquatic Toxicity Profile of Fullerene Derivatives.
Int. J. Mol. Sci. 2023, 24, 14160. https://doi.org/10.3390/ijms241814160

Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, and Emilio Benfenati,
Using the Correlation Intensity Index to build a model of cardiotoxicity.
Molecules 2023, 28, 6587. https://doi.org/10.3390/molecules28186587

Alla P. Toropova and Andrey A. Toropov,
Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity.
Amino Acids, (2023) 55:1437–1445. DOI: 10.1007/s00726-023-03322-0

Toropova, A.P.; Toropov, A.A.; Fjodorova, N.
QSPR and Nano-QSPR: Which One Is Common? The Case of Fullerenes Solubility.
Inorganics 2023, 11, 344. https://doi.org/10.3390/inorganics11080344

Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni, Emilio Benfenati,
In silico prediction of the mutagenicity of nitroaromatic compounds using correlation weights of fragments of local symmetry,
Mutation Research - Genetic Toxicology and Environmental Mutagenesis 891 (2023) 503684, https://doi.org/10.1016/j.mrgentox.2023.503684.

Alla P. Toropova, Andrey A. Toropov, Parvin Kumar, Ashwani Kumar, P. Ganga Raju Achary,
Fragments of local symmetry in a sequence of amino acids: Does one can use for QSPR/QSAR of peptides?
Journal of Molecular Structure, 1293, 2023, 136300. https://doi.org/10.1016/j.molstruc.2023.136300

Ramon Carbó-Dorca, Tanmoy Chakraborty,
Chapter 18 - Quantum similarity description of a unique classical and quantum QSPR algorithm in molecular spaces: the connection with Boolean hypercubes, algorithmic intelligence, and Gödel's incompleteness theorems,
Editor(s): Savas Kaya, László von Szentpály, Goncagül Serdaroglu, Lei Guo,
Chemical Reactivity, Elsevier, 2023, Pages 505-572, https://doi.org/10.1016/B978-0-32-390257-1.00025-5

A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati, D. Leszczynska, J. Leszczynski,
The validation of predictive potential via the system of self-consistent models: the simulation of blood-brain barrier permeation of organic compounds.
Journal of Molecular Modeling, 29 (2023) 218. https://doi.org/10.1007/s00894-023-05632-2

Toropov, A.A., Raskova, M., Raska, I., Toropova, A.P.
Chapter 1. Fundamentals of Mathematical Modeling of Chemicals Through QSPR/QSAR.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023, vol. 33, Pages 3–24. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_1

Toropov, A.A., Toropova, A.P.
Chapter 3. Application of SMILES to Cheminformatics and Generation of Optimum SMILES Descriptors Using CORAL Software.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023,vol 33, Pages 57-82. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_3

Nesmerák, K., Toropov, A.A.
Chapter 6. QSPR Models for Prediction of Redox Potentials Using Optimal Descriptors.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023,vol 33, Pages 139-166. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_6

Kudyshkin, V.O., Toropova, A.P.
Chapter 7. Building Up QSPR for Polymers Endpoints by Using SMILES-Based Optimal Descriptors.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023,vol 33, Pages 167-187. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_7

Behera, S.A., Toropova, A.P., Toropov, A.A., Achary, P.G.R.
Chapter 9. Quasi-SMILES-Based Mathematical Model for the Prediction of Percolation Threshold for Conductive Polymer Composites.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023,vol 33, Pages 211-239. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_9

Achary, P., Krishna, P., Toropova, A.P., Toropov, A.A.
Chapter 10. On the Possibility to Build up the QSAR Model of Different Kinds of Inhibitory Activity for a Large List of Human Intestinal Transporter Using Quasi-SMILES.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023, vol 33, Pages 241-268. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_10

Toropova, A.P., Toropov, A.A.
Chapter 14. The CORAL Software as a Tool to Develop Models for Nanomaterials’ Endpoints.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023,vol 33, Pages 351-371. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_14

Toropov, A.A., Toropova, A.P., Leszczynska, D., Leszczynski, J.
Chapter 16. On Complementary Approaches of Assessing the Predictive Potential of QSPR/QSAR Models.
In: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023, vol 33, Pages 397-420. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4_16

In Book: Toropova, A.P., Toropov, A.A. (eds)
QSPR/QSAR Analysis Using SMILES and Quasi-SMILES.
Challenges and Advances in Computational Chemistry and Physics,
2023, vol 33, pp.1-467. Springer, Cham. https://doi.org/10.1007/978-3-031-28401-4

Danieli, A.; Colombo, E.; Raitano, G.; Lombardo, A.; Roncaglioni, A.; Manganaro, A.; Sommovigo, A.; Carnesecchi, E.; Dorne, J.-L.C.M.; Benfenati, E.
The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models.
Int. J. Mol. Sci. 2023, 24, 9894. https://doi.org/10.3390/ijms24129894

Gulcin Tugcu, Hande Sipahi, Mohammad Charehsaz, Ahmet Aydin, Melek Türker Saçan,
Chapter 20 - Computational toxicology of pharmaceuticals,
Editor(s): K. Roy,
Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development, Academic Press, 2023, Pages 519-537, https://doi.org/10.1016/B978-0-443-18638-7.00007-4

Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati,
The enhancement scheme for the predictive ability of QSAR: a case of mutagenicity.
Toxicology in Vitro, 91, 2023, 105629. https://doi.org/10.1016/j.tiv.2023.105629

A.A. Toropov, A.P. Toropova, P.G.R. Achary,
Prediction of n-octanol-water partition coefficient of platinum (IV) complexes using correlation weights of fragments of local symmetry.
Structural Chemistry, 34, 1517-1526 (2023). https://doi.org/10.1007/s11224-023-02197-x

A.A. Toropova, A.P. Toropova, D. Leszczynska, J. Leszczynski,
Development of self-consistency models of anticancer activity of nanoparticles that were observed under different experimental conditions using quasi-SMILES.
Nanomaterials, 2023, 13(12), 1852. https://doi.org/10.3390/nano13121852

Nilima R. Das, Tripti Sharma, Anshuman Chandra,Vijay Kumar Goel, Andrey A. Toropov, Alla P. Toropova, P.Ganga Raju Achary,
Isoprenylcysteine Carboxyl Methyltransferase Inhibitors: QSAR, Docking and Molecular Dynamics Studies.
Journal of Molecular Structure 1291 (2023) 135966. https://doi.org/10.1016/j.molstruc.2023.135966

Xiliang Yan, Tongtao Yue, David A. Winkler, Yongguang Yin, Hao Zhu, Guibin Jiang, and Bing Yan,
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation.
Chemical Reviews, 2023, 123, 13, 8575–8637. https://doi.org/10.1021/acs.chemrev.3c00070

A.A. Toropov, A.P. Toropova, A. Roncaglioni, E. Benfenati,
Does the accounting of the local symmetry fragments in SMILES improve the predictive potential of the QSPR-model for Henry's law constants?
Environmental Science: Advances, 2023, 2, 916 - 921. https://doi.org/10.1039/D3VA00012E

J. Meneses, M. González-Durruthy, E. Fernandez-de Gortari, A.P. Toropova, A.A. Toropov, E. Alfaro-Moreno.
A Nano-QSTR model to predict nano-cytotoxicity: an approach using human lung cells data.
Particle and Fibre Toxicology, (2023) 20: 21. https://doi.org/10.1186/s12989-023-00530-0

A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati, D. Leszczynska, J. Leszczynski,
CORAL: Model of ecological impact of heavy metals on soils via the study of modification of concentration of biomolecules in Earthworms (Eisenia fetida).
Archives of Environmental Contamination and Toxicology, (2023) 84:504-515. https://doi.org/10.1007/s00244-023-01001-5

A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati,
The system of self-consistent models: QSAR analysis of drug-induced liver toxicity.
Toxics, 2023; 11(5): 419. https://doi.org/10.3390/toxics11050419

A.A. Toropov, A.P. Toropova, A. Roncaglioni, E. Benfenati,
The system of self-consistent models for pesticide toxicity to Daphnia Magna,
Toxicology Mechanisms and Methods, 2023, 33:7, 578-583. DOI: 10.1080/15376516.2023.2197487

Andrey A. Toropov, Devon Barnes, Alla P. Toropova, Alessandra Roncaglioni, Alasdair R. Irvine, Rosalinde Masereeuw, Emilio Benfenati,
CORAL models for drug induced nephrotoxicity.
Toxics, 2023, 11, 293. https://doi.org/10.3390/toxics11040293

Rahul Singh, Parvin Kumar, Jayant Sindhu, Meena Devi, Ashwani Kumar, Sohan Lal, Devender Singh,
Parsing structural fragments of thiazolidin-4-one based a-amylase inhibitors: A combined approach employing in vitro colorimetric screening and GA-MLR based QSAR modelling supported by molecular docking, molecular dynamics simulation and ADMETstudies,
Computers in Biology and Medicine, 157, 2023, 106776, https://doi.org/10.1016/j.compbiomed.2023.106776

Zhengtao Zhou, Mario Eden, and Weifeng Shen,
Treat Molecular Linear Notations as Sentences: Accurate Quantitative Structure–Property Relationship Modeling via a Natural Language Processing Approach.
Ind. Eng. Chem. Res. 2023, 62, 12, 5336–5346, https://doi.org/10.1021/acs.iecr.2c04070

A.P. Toropova, A.A. Toropov, A. Roncaglioni, E. Benfenati,
Binding organophosphate pesticides to acetylcholinesterase: Risk assessment using the Monte Carlo method.
Toxicological & Environmental Chemistry, 2023, 105(1-7), 19-27. DOI: 10.1080/02772248.2023.2181348

Nilima R. Das, Tripti Sharma, Andrey A. Toropov, Alla P. Toropova, P. Ganga Raju Achary,
Machine-Learning Technique, QSAR, and Molecular Dynamics for hERG-Drug Interactions.
Journal of Biomolecular Structure & Dynamics, 41:23, (2023) 13766-13791. https://doi.org/10.1080/07391102.2023.2193641

Toropova, A.P.; Toropov, A.A.; Fjodorova, N.
In Silico Simulation of Impacts of Metal Nano-Oxides on Cell Viability in THP-1 cells Based on the Correlation Weights of the Fragments of Molecular Structures and Codes of Experimental Conditions Represented by Means of Quasi-SMILES.
Int. J. Mol. Sci. 2023, 24, 2058. https://doi.org/10.3390/ijms24032058

Toropov, A.A., Di Nicola, M.R., Toropova, A.P., Roncaglioni, A., Dorne, J.L.C.M., Benfenati, E.
Quasi-SMILES: Self-consistent models for toxicity of organic chemicals to tadpoles.
Chemosphere 312 (2023) 137224. https://doi.org/10.1016/j.chemosphere.2022.137224

Nilima R. Das, Tripti Sharma, Ayeshkant Mallick, Alla P. Toropova, Andrey A. Toropov, and P. Ganga Raju Achary,
Computational Approach in Designing and Development of Novel Inhibitors of AKR1C1.
Chapter 32, In Book: Tripti Swarnkar, Srikanta Patnaik, Indian Institute of Technology (IIT), Sanjay Misra, Siksha O. (Eds), Ambient Intelligence in Health Care: Proceedings of ICAIHC 2022: 317 (Smart Innovation, Systems and Technologies, 317). Springer; 2023, vol 317, pp. 325-337.
https://link.springer.com/chapter/10.1007/978-981-19-6068-0_32

Alla P. Toropova and Andrey A. Toropov,
Quasi-SMILES as a basis to build up models of endpoints for nanomaterials.
Environmental Technology, 44(28), 2023, 4460-4467. https://doi.org/10.1080/09593330.2022.2093655

Feifan Li, Guohui Sun, Tengjiao Fan, Na Zhang, Lijiao Zhao, Rugang Zhong, Yongzhen Peng,
Ecotoxicological QSAR modelling of the acute toxicity of fused and non-fused polycyclic aromatic hydrocarbons (FNFPAHs) against two aquatic organisms: Consensus modelling and comparison with ECOSAR,
Aquatic Toxicology, 255, 2023, 106393, https://doi.org/10.1016/j.aquatox.2022.106393

• TO THE BEGINNING

2022

Deoghuria, Sayandeep and Aastha Mahapatra, Nilima R. Das, P. Ganga Raju Achary, and Tripti Sharma.
"QSAR Study, Molecular Docking, and Pharmacokinetic Analysis of Substituted Dihydropyrimidinone as ErbB2 Inhibitors,"
International Journal of Quantitative Structure-Property Relationships (IJQSPR). 2022, 7, no.1: 1-17.
http://doi.org/10.4018/IJQSPR.315630

Samuel J. Belfield, James W. Firman, Steven J. Enoch, Judith C. Madden, Knut Erik Tollefsen, Mark T.D. Cronin,
A Review of Quantitative Structure-Activity Relationship Modelling Approaches to Predict the Toxicity of Mixtures,
Computational Toxicology, 2022, 100251,https://doi.org/10.1016/j.comtox.2022.100251

Shahin Ahmadi, Azizeh Abdolmaleki, Marjan Jebeli Javan,
In silico study of natural antioxidants,
Vitamins and Hormones, Academic Press, 2022, https://doi.org/10.1016/bs.vh.2022.09.001

Parastar H, Tauler R.
Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists.
Angew Chem Int Ed Engl. 2022 Nov 2;61(44):e201801134. doi: 10.1002/anie.201801134

Rahul Singh, Parvin Kumar, Meena Devi, Sohan Lal, Ashwani Kumar, Jayant Sindhu, Alla P. Toropova, Andrey A. Toropov and Devender Singh,
Monte Carlo Based QSGFEAR: Prediction of Gibb’s Free Energy of Activation at Different Temperatures Using SMILES Based Descriptors.
New Journal of Chemistry, 2022, 46, 19062-19072. https://doi.org/10.1039/D2NJ03515D

Alla P. Toropova, Andrey A. Toropov, Natalja Fjodorova,
Quasi-SMILES for predicting toxicity of Nano-mixtures to Daphnia Magna.
NanoImpact 28 (2022) 100427. https://doi.org/10.1016/j.impact.2022.100427

Sang L, Wang Y, Zong C, Wang P, Zhang H, Guo D, Yuan B, Pan Y.
Machine Learning for Evaluating the Cytotoxicity of Mixtures of Nano-TiO₂ and Heavy Metals: QSAR Model Apply Random Forest Algorithm after Clustering Analysis.
Molecules. 2022; 27(18):6125. https://doi.org/10.3390/molecules27186125

Parvin Kumar, Rahul Singh, Ashwani Kumar, Alla P. Toropova, Andrey A. Toropov, Meena Devi, Sohan Lal, Jayant Sindhu, Devender Singh,
Identifications of Good and Bad Structural Fragments of Hydrazone/2,5-Disubstituted-1,3,4-oxadiazole Hybrids with correlation intensity index and consensus modelling using Monte Carlo Based QSAR Studies.
SAR and QSAR in Environmental Research, 33(9), 2022, 677-700. https://doi.org/10.1080/1062936X.2022.2120068

Nilima R. Das, Krishnendu Bera, Tripti Sharma, Alla P. Toropova, Andrey A. Toropov, P. Ganga Raju Achary,
Computational approach for building QSAR models for inhibition of HIF-1A,
Journal of the Indian Chemical Society, Volume 99, Issue 10, 2022, 100687, https://doi.org/10.1016/j.jics.2022.100687.

E. Wyrzykowska, A. Mikolajczyk, I. Lynch, N. Jeliazkova, N. Kochev, H. Sarimveis, P. Doganis, P. Karatzas, A. Afantitis, G. Melagraki, A. Serra, D. Greco, J. Subbotina, V. Lobaskin, M. A. Bañares, E. Valsami-Jones, K. Jagiello & T. Puzyn.
Representing and describing nanomaterials in predictive nanoinformatics.
Nat. Nanotechnol. 17, pages 924–932 (2022) https://doi.org/10.1038/s41565-022-01173-6

Jing Li, Chuanxi Wang, Le Yue, Feiran Chen, Xuesong Cao, Zhenyu Wang,
Nano-QSAR modeling for predicting the cytotoxicity of metallic and metal oxide nanoparticles: A review,
Ecotoxicology and Environmental Safety, 243, 2022, 113955, https://doi.org/10.1016/j.ecoenv.2022.113955.

Nazarova AL, Nakano A.
VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling.
Machine Learning and Knowledge Extraction. 2022; 4(3):715-737. https://doi.org/10.3390/make4030034

Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati,
Monte Carlo technique to study of the adsorption affinity of azo dyes with applying new statistical criteria of the predictive potential.
SAR and QSAR in Environmental Research, 33:8, 2022, 621-630. DOI: 10.1080/1062936X.2022.2104369

G. Selvestrel, G.J. Lavado, A.P. Toropova, A.A. Toropov, D. Gadaleta, M. Marzo, D. Baderna, E. Benfenati,
Monte Carlo Models for Sub-Chronic Repeated-Dose Toxicity: Systemic and Organ-Specific Toxicity.
International Journal of Molecular Sciences, 2022, 23, 6615. https://doi.org/10.3390/ijms23126615

Filip Stoliński, Anna Rybińska-Fryca, Maciej Gromelski, Alicja Mikolajczyk & Tomasz Puzyn.
NanoMixHamster: a web-based tool for predicting cytotoxicity of TiO₂-based multicomponent nanomaterials toward Chinese hamster ovary (CHO-K1) cells,
Nanotoxicology, 16(3), 2022, 276-289. DOI: 10.1080/17435390.2022.2080609

A.A. Toropov, F. Kjeldsen, A.P. Toropova,
Use of quasi-SMILES to build models based on quantitative results from experiments with nanomaterials.
Chemosphere 303 (2022) 135086. https://doi.org/10.1016/j.chemosphere.2022.135086

Lebre, F.; Chatterjee, N.; Costa, S.; Fernández-de-Gortari, E.; Lopes, C.; Meneses, J.; Ortiz, L.; Ribeiro, A.R.; Vilas-Boas, V.; Alfaro-Moreno, E.
Nanosafety: An Evolving Concept to Bring the Safest Possible Nanomaterials to Society and Environment.
Nanomaterials 2022, 12, 1810. https://doi.org/10.3390/nano12111810

A.P. Toropova, A.A. Toropov, E.L. Viganò, E. Colombo, A. Roncaglioni, E. Benfenati,
Carcinogenicity Prediction Using the Index of Ideality of Correlation.
SAR and QSAR in Environmental Research, 2022; 33(6), 419-428. DOI:10.1080/1062936X.2022.2076736

Roncaglioni A, Lombardo A, Benfenati E.
The VEGAHUB Platform: The Philosophy and the Tools.
Alternatives to Laboratory Animals.
2022; 50(2): 121-135. doi:10.1177/02611929221090530

Natalia Lidmar von Ranke, Reinaldo Barros Geraldo, Andre Lima dos Santos, Victor G.O. Evangelho, Flaminia Flammini, Lucio Mendes Cabral, Helena Carla Castro, Carlos Rangel Rodrigues,
Applying in silico Approaches to Nanotoxicology: Current Status and Future Potential,
Computational Toxicology, Volume 22, 2022, 100225. https://doi.org/10.1016/j.comtox.2022.100225.

Andrey A. Toropov, Matteo R. Di Nicola, Alla P. Toropova, Alessandra Roncaglioni, Edoardo Carnesecchi, Nynke I. Kramer, Antony J. Williams, Manuel E. Ortiz-Santaliestra, Emilio Benfenati, Jean-Lou C.M. Dorne,
A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians,
Science of the Total Environment 830 (2022) 154795. https://doi.org/10.1016/j.scitotenv.2022.154795

Andrey A. Toropov, Alla P. Toropova, P. Ganga Raju Achary, Maria Raškova, Ivan Raška Jr.
The searching for agents for Alzheimer's disease treatment via the system of self-consistent models.
Toxicology Mechanisms and Methods, 32:7, (2022) 549-557. https://doi.org/10.1080/15376516.2022.2053918

In Silico Methods for Predicting Drug Toxicity. Ed. Benfenati, E., Humana, New York, NY. 2022. https://doi.org/10.1007/978-1-0716-1960-5

N. Fjodorova, M. Novič, K. Venko, V. Drgan, B. Rasulev, M. Türker Saçan, S.S. Erdem, G. Tugcu, A.P. Toropova, A.A. Toropov,
How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases.
Computational and Structural Biotechnology Journal, 20 (2022) 913–924. https://doi.org/10.1016/j.csbj.2022.02.006

A.P. Toropova, A.A. Toropov,
Nanomaterials: quasi-SMILES as a flexible basis for regulation and environmental risk assessment.
Science of the Total Environment 823 (2022) 153747. https://doi.org/10.1016/j.scitotenv.2022.153747

K. Nesměrák, A.A. Toropov, I. Yildiz,
QSAR based on hybrid optimal descriptors as a tool to predict antibacterial activity against Staphylococcus aureus.
Front. Biosci. (Landmark Ed), 2022; 27(4): 112. http://doi.org/10.31083/j.fbl2704112

Kimia Jafari, Mohammad Hossein Fatemi, Alla P. Toropova, Andrey A. Toropov,
The development of nano-QSPR models for viscosity of nanofluids using the index of ideality of correlation and the correlation intensity index,
Chemometrics and Intelligent Laboratory Systems 222 (2022) 104500. https://doi.org/10.1016/j.chemolab.2022.104500

A.P. Toropova, A.A. Toropov, A. Roncaglioni, and E. Benfenati,
The system of self-consistent models of vapour pressure.
Chemical Physics Letters, 790 (2022) 139354. https://doi.org/10.1016/j.cplett.2022.139354

Zuowei Ji, Wenjing Guo, Erin L. Wood, Jie Liu, Sugunadevi Sakkiah, Xiaoming Xu, Tucker A. Patterson, and Huixiao Hong
Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.
Chemical Research in Toxicology, 2022, 35, 2, 125–139. DOI: 10.1021/acs.chemrestox.1c00310

Andrey A. Toropov, Alla P. Toropova, Valentin O. Kudyshkin,
The system of self-consistent QSPR-models for refractive index of polymers.
Structural Chemistry, 2022 ; 33 : 617-624. https://doi.org/10.1007/s11224-021-01875-y

Giovanna J. Lavado, Diego Baderna, Edoardo Carnesecchi, Alla P. Toropova, Andrey A. Toropov, Jean Lou CM Dorne, Emilio Benfenati,
QSAR models for soil ecotoxicity: development and validation of models to predict reproductive toxicity of organic chemicals in the collembola Folsomia candida.
Journal of Hazardous Materials 423 (2022) 127236. https://doi.org/10.1016/j.jhazmat.2021.127236

Zhu, T., Tao, C.
Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor
Journal of Hazardous Materials, 423, (2022) 127037. DOI: 10.1016/j.jhazmat.2021.127037

Qingzhu Jia, Junli Wang, Fangyou Yan, Qiang Wang,
A QSTR model for toxicity prediction of pesticides towards Daphnia magna,
Chemosphere, 291, 2022, 132980. https://doi.org/10.1016/j.chemosphere.2021.132980

Nath, A., De, P., Roy, K.
QSAR modelling of inhalation toxicity of diverse volatile organic molecules using no observed adverse effect concentration (NOAEC) as the endpoint
Chemosphere, 287,(2022) 131954. DOI: 10.1016/j.chemosphere.2021.131954

Hdoufane, I., Ounaissi, D., Dermoune, A., Cherqaoui, D.
Development of QSAR models using singular value decomposition method: A case study for predicting anti-hiv-1 and anti-HCV biological activities
Biointerface Research in Applied Chemistry, 12 (3), (2022) pp. 3090-3105. DOI: 10.33263/BRIAC123.30903105

Matsuzaka, Y., Uesawa, Y.
A molecular image-based novel quantitative structure-activity relationship approach, deepsnap-deep learning and machine learning
Current Issues in Molecular Biology, 42,(2022) pp. 455-472. DOI: 10.21775/cimb.042.455

A.P. Toropova, A.A. Toropov, A. Lombardo, G. Lavado, and E. Benfenati,
Paradox of "ideal correlations": improved model for air half-life of persistent organic pollutants.
Environmental Technology 2022, Vol. 43, No. 16, 2510-2515. DOI: 10.1080/09593330.2021.1882588

Andrey A. Toropov, Alla P. Toropova, Aleksandar Veselinović, Danuta Leszczynska, Jerzy Leszczynski,
SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model.
Journal of Biomolecular Structure and Dynamics, 2022, 40(2), 780-786. DOI: 10.1080/07391102.2020.1818627

• TO THE BEGINNING

2021

Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni, Emilio Benfenati
The system of self-consistent semi-correlations as one of the tools of cheminformatics for design antiviral drugs.
New Journal of Chemistry, 2021, 45, 20713 - 20720. https://doi.org/10.1039/D1NJ03394H

A. P. Toropova, A. A. Toropov, E. Benfenati,
Semi-correlations as a tool to model for skin sensitization.
Food and Chemical Toxicology, 157 (2021) 112580. https://doi.org/10.1016/j.fct.2021.112580

Bule, M, Jalalimanesh, N, Bayrami, Z, Baeeri, M, Abdollahi, M.
The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools.
Chem Biol Drug Des. 2021; 98: 954– 967. https://doi.org/10.1111/cbdd.13750

Santana Ricardo, Onieva Enrique , Zuluaga Robin , Duardo-Sánchez Aliuska and Gañán Piedad,
The Role of Machine Learning in Centralized Authorization Process of Nanomedicines in European Union,
Current Topics in Medicinal Chemistry 2021; 21(9), 828 - 838. https://dx.doi.org/10.2174/1568026621666210319101847

A.P. Toropova, A.A. Toropov, D. Leszczynska, J. Leszczynski,
Application of quasi-SMILES to the model of gold-nanoparticles uptake in A549 cells.
Computers in Biology and Medicine 136 (2021) 104720. https://doi.org/10.1016/j.compbiomed.2021.104720

Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati,
The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method.
SAR and QSAR in Environmental Research, 32(9), (2021) 689-698. DOI:10.1080/1062936X.2021.1952649

Andrey A. Toropov, Alla P. Toropova,
The system of self-consistent models for the uptake of nanoparticles in PaCa2 cancer cells.
Nanotoxicology, 15:7, 2021, 995-1004. DOI:10.1080/17435390.2021.1951387

Selvestrel G, Robino F, Baderna D, Manganelli S, Asturiol D, Manganaro A, Zanotti Russo M, Lavado G, Toma C, Roncaglioni A, Benfenati E
SpheraCosmolife: a new tool for the risk assessment of cosmetic products.
ALTEX 2021, 38(4), pp. 565-579. doi:10.14573/altex.2010221

A.P. Toropova and A.A. Toropov,
The system of self-consistent of models: a new approach to build up and validation of predictive models of the octanol/water partition coefficient for gold nanoparticles.
Int. J. Environ. Res. 15(4), 2021, 709-722. DOI: 10.1007/s41742-021-00346-w

Crisan, L., Borota, A., Bora, A., Funar-Timofei, S. and Ilia, G.
(2021). Chemometric Modeling of Algal and Daphnia Toxicity.
In Chemometrics and Cheminformatics in Aquatic Toxicology, K. Roy (Ed.). https://doi.org/10.1002/9781119681397.ch13

Larionov, A.; Nezhnikova, E.; Smirnova, E.
Risk Assessment Models to Improve Environmental Safety in the Field of the Economy and Organization of Construction: A Case Study of Russia.
Sustainability 2021, 13, 13539. https://doi.org/10.3390/su132413539

Halder, A.K. and Cordeiro, M.N.D.S.
(2021). Chemometric Modeling of Daphnia Toxicity.
In Chemometrics and Cheminformatics in Aquatic Toxicology, K. Roy (Ed.). https://doi.org/10.1002/9781119681397.ch15

Oh Lee, Y. and Sung, B.
(2021). In Silico Platforms for Predictive Ecotoxicology.
In Chemometrics and Cheminformatics in Aquatic Toxicology, K. Roy (Ed.). https://doi.org/10.1002/9781119681397.ch23

Quevedo-Tumailli, V.; Ortega-Tenezaca, B.; González-Díaz, H.
IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.
Int. J. Mol. Sci. 2021, 22, 13066. https://doi.org/10.3390/ijms222313066

Singh, A.V., Rosenkranz, D., Ansari, M.H.D., Singh, R., Kanase, A., Singh, S.P., Johnston, B., Tentschert, J., Laux, P. and Luch, A. Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction. Adv. Intell. Syst.,(2020), 2: 2000084. https://doi.org/10.1002/aisy.202000084

Alla P. Toropova, Andrey A. Toropov, Jerzy Leszczynski, Natalia Sizochenko,
Using quasi-SMILES for the predictive modeling of the safety of 574 metal oxide nanoparticles measured in different experimental conditions.
Environmental Toxicology and Pharmacology 86 (2021) 103665. DOI: 10.1016/j.etap.2021.103665

A.A. Toropov, A.P. Toropova, A. Lombardo, A. Roncaglioni, G. Lavado, E. Benfenati,
The Monte Carlo method to build up models of the hydrolysis half-lives of organic compounds.
SAR and QSAR in Environmental Research, 2021, 32:6, 463-471. DOI: 10.1080/1062936X.2021.1914156

Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki,
Computational enrichment of physicochemical data for the development of a ζ-potential read-across predictive model with Isalos Analytics Platform,
NanoImpact, 22, 2021, 100308. https://doi.org/10.1016/j.impact.2021.100308

A.P. Toropova, A.A. Toropov, E. Benfenati,
The self-organizing vector of atom-pairs proportions: use to develop models for melting points.
Structural Chemistry (2021) 32: 967–971. https://doi.org/10.1007/s11224-021-01778-y

Alla P. Toropova, Andrey A. Toropov, Alessandra Roncaglioni, Emilio Benfenati,
The index of ideality of correlation improves the predictive potential of models of the antioxidant activity of tripeptides from frog skin (Litoria rubella).
Computers in Biology and Medicine, 133 (2021) 104370. https://doi.org/10.1016/j.compbiomed.2021.104370

Baderna, D.; Faoro, R.; Selvestrel, G.; Troise, A.; Luciani, D.; Andres, S.; Benfenati, E.
Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools.
Molecules 2021, 26, 1928. https://doi.org/10.3390/molecules26071928

González-Durruthy, M.; Concu, R.; Ruso, J.M.; Cordeiro, M.N.D.S.
New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches.
Biology 2021, 10, 171. https://doi.org/10.3390/biology10030171

Toropova, A.P., Toropov, A.A.
Can the Monte Carlo method predict the toxicity of binary mixtures?
Environ Sci Pollut Res (2021) 28: 39493–39500. https://doi.org/10.1007/s11356-021-13460-1

B.Ortega-Tenezaca and H. González-Díaz,
IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks.
Nanoscale, 2021, 13, 1318-1330. https://doi.org/10.1039/D0NR07588D

Duchowicz, P. R., Fioressi, S., Romanelli, G., & Bacelo, D. E.
Alternative QSAR Study for Unsymmetrical Aromatic Disulfide Anti-SARS Inhibitors.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), 6(2), (2021) 47-57. doi:10.4018/IJQSPR.2021040104

Benfenati, E., Roncaglioni, A., Carnesecchi, E., Mazzucotelli, M., Marzo, M., Toropov, A.A., Toropova, A.P., Baldin, R., Ciacci, A., Kovarich, S., Sartori, L., Yang, C., Magdziarz, T., Hobocienski, B., Mostrag, A.,
Maintenance, update and further development of EFSA's Chemical Hazards: OpenFoodTox 2.0.
EFSA supporting publication 2021: 18(3): EN-6476. 46pp. doi:10.2903/sp.efsa.2021.EN-6476

Andrey A. Toropov and Alla P. Toropova
The unreliability of the reliability criteria in the estimation of QSAR for skin sensitivity: a pun or a reliable law?
Toxicology Letters, (2021) 340, 133-140. https://doi.org/10.1016/j.toxlet.2021.01.015

Andrey A. Toropov and Alla P. Toropova
Quasi-SMILES as a basis for the development of models for the toxicity of ZnO nanoparticles.
Science of the Total Environment, 772 (2021) 145532. https://doi.org/10.1016/j.scitotenv.2021.145532

J.L.C.M. Dorne, J. Richardson, A. Livaniou, E. Carnesecchi, L. Ceriani, R. Baldin, S. Kovarich, M. Pavan, E. Saouter, F. Biganzoli, L. Pasinato, M. Zare Jeddi, T. P. Robinson, G.E.N. Kass, A.K.D. Liem, A.A. Toropov, A.P. Toropova, C. Yang, A. Tarkhov, N. Georgiadis, M.R. Di Nicola, A. Mostrag, H. Verhagen, A. Roncaglioni, E. Benfenati, A. Bassan.
EFSA’s OpenFoodTox: An open source toxicological database on chemicals in food and feed and its future developments.
Environment International 146 (2021) 106293. https://doi.org/10.1016/j.envint.2020.106293

Costa, P.C.S.; Evangelista, J.S.; Leal, I.; Miranda, P.C.M.L.
Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR.
Mathematics 2021, 9, 60. https://doi.org/10.3390/math9010060

P.G.R. Achary, A. P. Toropova, A.A. Toropov,
Prediction of the self-accelerating decomposition temperature of organic peroxides.
Process Safety Progress, 2021; 40: e12189. DOI: 10.1002/prs.12189

Alla P. Toropova, Maria Raškova, Ivan Raška Jr., Andrey A. Toropov,
The sequence of amino acids as the basis for the model of biological activity of peptides.
Theoretical Chemistry Accounts, 140, 15 (2021). DOI: 10.1007/s00214-020-02707-8

A. Worachartcheewan, A. P. Toropova, A. A. Toropov, R. Pratiwi, V. Prachayasittikul, C. Nantasenamat,
Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2.
Combinatorial Chemistry & High Throughput Screening, 24 (8), 2021, 1217 - 1228. DOI: 10.2174/1386207323666200902141907

A.A. Toropov, A.P. Toropova, M. Marzo, E. Carnesecchi, G. Selvestrel, E. Benfenati,
Pesticides, Cosmetics, Drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity.
Molecular Diversity, 25, 1137–1144 (2021). DOI: 10.1007/s11030-020-10085-3

Gelmboldt, V., Ognichenko, L., Shyshkin, I., Kuz’min, V.
QSPR models for water solubility of ammonium hexafluorosilicates: analysis of the effects of hydrogen bonds.
Struct Chem. 32, pages 309–319,(2021). https://doi.org/10.1007/s11224-020-01652-3

Gadaleta, D.; Marzo, M.; Toropov, A.A.; Toropova, A.P.; Lavado, G.; Escher, S.; Dorne, J.-L.; Benfenati, E.
Integrated in silico models for the prediction of No-Observed-(Adverse)-Effect-Levels and Lowest-Observed-(Adverse)-Effect-Levels in rats for sub-chronic repeated dose toxicity.
Chemical Research in Toxicology, 2021, 34, 2, 247–257. https://doi.org/10.1021/acs.chemrestox.0c00176

Ashwani Kumar, Jayant Sindhu & Parvin Kumar,
In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N-myristoyltransferase using Monte Carlo method with index of ideality of correlation,
Journal of Biomolecular Structure and Dynamics, 2021, 39:14, 5014-5025. DOI: 10.1080/07391102.2020.1784286

Sk. Abdul Amin, Kalyan Ghosh, Shovanlal Gayen & Tarun Jha,
Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors,
Journal of Biomolecular Structure and Dynamics, 2021, 39:13, 4764-4773. DOI: 10.1080/07391102.2020.1780946

Ahmed S, Islam N, Shahinozzaman M, Fakayode SO, Afrin N, Halim MA.
Virtual screening, molecular dynamics, density functional theory and quantitative structure activity relationship studies to design peroxisome proliferator-activated receptor-? agonists as anti-diabetic drugs.
J. Biomol. Struct. Dyn. 2021; 39:2, 728-742. doi:10.1080/07391102.2020.1714482

Ahmadi, S., Ghanbari, H., Lotfi, S., Azimi, N.,
Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method.
Mol Divers. 2021, 25(1), pp. 87–97 https://doi.org/10.1007/s11030-019-10026-9

• TO THE BEGINNING

2020

Buglak, A.A.; Samokhvalov, A.V.; Zherdev, A.V.; Dzantiev, B.B.
Methods and Applications of In Silico Aptamer Design and Modeling.
Int. J. Mol. Sci. 2020, 21, 8420.

Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.
Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform.
Nanomaterials 2020, 10, 2017. https://doi.org/10.3390/nano10102017

Santana, R., Zuluaga, R., Gañán, P., Arrasate, S., Onieva, E., González-Díaz, H.,
Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models,
Nanoscale, 2020,12, 13471-13483.

Przybylek, M.
Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening.
Molecules 2020, 25, 5942. https://doi.org/10.3390/molecules25245942

Duchowicz, P.R., Aranda, J.F., Bacelo, D.E., Fioressi, S.E.
QSPR study of the Henry's law constant for heterogeneous compounds
Chemical Engineering Research and Design, 154,(2020) pp. 115-121. https://www.sciencedirect.com/science/article/abs/pii/S0263876219305763?via%3Dihub

Bouhedjar, K, Boukelia, A, Khorief Nacereddine, A, Boucheham, A, Belaidi, A, Djerourou, A.
A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling.
Chem Biol Drug Des. 2020; 96: 961– 972. https://doi.org/10.1111/cbdd.13742

A.A. Toropov, A.P. Toropova, G. Selvestrel, D. Baderna, E. Benfenati,
Prediction of No Observed Adverse Effect Concentration for Inhalation toxicity: Monte Carlo approach.
SAR and QSAR in Environmental Research, 31(12), 2020, 1-12. DOI: 10.1080/1062936X.2020.1841827

A.P. Toropova, A.A. Toropov, D. Leszczynska, J. Leszczynski,
How the CORAL software can be used to select compounds for treatment of neurodegenerative diseases?
Toxicology and Applied Pharmacology 408 (2020) 115276. https://doi.org/10.1016/j.taap.2020.115276

A. Rybińska-Fryca, A. Mikolajczyk, and T. Puzyn,
Structure–activity prediction networks (SAPNets): a step beyond Nano-QSAR for effective implementation of the safe-by-design concept.
Nanoscale, 2020,12, 20669-20676. https://doi.org/10.1039/D0NR05220E

Andrey A.Toropov, Alla P.Toropova, Emilio Benfenati,
QSAR model for pesticides toxicity to Rainbow Trout based on “ideal correlations”.
Aquatic Toxicology 227 (2020) 105589. https://doi.org/10.1016/j.aquatox.2020.105589

O.V. Tinkov, V.Y. Grigorev, A.N. Razdolsky, L.D. Grigoryeva & J.C. Dearden,
Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna,
SAR and QSAR in Environmental Research, (2020) 31:8, 615-641, DOI: 10.1080/1062936X.2020.1791250

Alla P. Toropova, Andrey A. Toropov,
Extending of QSPR/QSAR-algorithms in order to apply to nanomaterials.
MDPI AG in MOL2NET 2020, International Conference on Multidisciplinary Sciences, 6th edition session NANOBIOMATJND-02: JSU-NDSU Nanotech. & BioMaterials Science Workshop, Jackson & Fargo, USA, 2020.
Published: 28 July 2020. DOI: 10.3390/mol2net-06-06890

Andrey A. Toropov, Natalia Sizochenko, Alla P. Toropova, Danuta Leszczynska, Jerzy Leszczynski,
Advancement of predictive modeling of zeta potentials in metal oxide nanoparticles with correlation intensity index (CII).
Journal of Molecular Liquids, 317 (2020) 113929. https://doi.org/10.1016/j.molliq.2020.113929

Claudia Ileana Cappelli, Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati,
Ecosystem ecology: models for acute toxicity of pesticides towards Daphnia magna.
Environmental Toxicology and Pharmacology 80 (2020) 103459. https://doi.org/10.1016/j.etap.2020.103459

González-Durruthy, M.; Concu, R.; Ruso, J.; Dias Soeiro Cordeiro, M.N.
New Mechanistic Insights on Carbon Nanotubes Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches.
Preprints 2020, 2020090014 (doi: 10.20944/preprints202009.0014.v1).

Shahin Ahmadi, Alla P. Toropova, and Andrey A. Toropov,
Correlation Intensity Index: Mathematical modelling of cytotoxicity of metal oxide nanoparticles.
Nanotoxicology, 2020, 14:8, 1118-1126.DOI: 10.1080/17435390.2020.1808252

Timothy Clark, Martin G. Hicks
Models of necessity.
Beilstein J. Org. Chem. 2020, 16, 1649–1661. doi:10.3762/bjoc.16.137

Lotfi, S., Ahmadi, S. & Zohrabi, P.
QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants.
Struct Chem 31, pages 2257–2270 (2020). https://doi.org/10.1007/s11224-020-01568-y

A.A. Toropov, A.P. Toropova, E. Benfenati,
“Ideal correlations” for the predictive toxicity to Tetrahymena pyriformis.
Toxicology Mechanisms and Methods, 30(8), 2020, 605-610. DOI: 10.1080/15376516.2020.1801928

Singh, A. V., Ansari, M. H. D., Rosenkranz, D., Maharjan, R. S., Kriegel, F. L., Gandhi, K., Kanase, A., Singh, R., Laux, P., Luch, A.,
Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine.
Adv. Healthcare Mater. 2020, 1901862. https://doi.org/10.1002/adhm.201901862

A. A. Toropov, A. P. Toropova, V. O. Kudyshkin, N. I. Bozorov, S. Sh. Rashidova,
Applying of the Monte Carlo technique to build up models of glass transition temperatures of diverse polymers.
Structural Chemistry, (2020) 31: 1739-1743. DOI: 10.1007/s11224-020-01588-8

Maja Zivkovic, Marko Zlatanovic, Nevena Zlatanovic, Mladjan Golubović, Aleksandar M. Veselinović,
The application of the combination of Monte Carlo optimization method based QSAR modeling and molecular docking in drug design and development.
Mini-Reviews in Medicinal Chemistry, 2020, 20(14), 1389-1402. DOI: 10.2174/1389557520666200212111428

Kumar, A., Kumar, P.
Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in monte carlo optimization.
Arch. Toxicol., 94, pages 3069–3086 (2020). https://doi.org/10.1007/s00204-020-02828-w

Ly Ly Pham, Sean M. Watford, Prachi Pradeep, Matthew T. Martin, Russell S. Thomas, Richard S. Judson, R. Woodrow Setzer, Katie Paul Friedman,
Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels.
Computational Toxicology, 15, 2020, 100126. https://doi.org/10.1016/j.comtox.2020.100126

G.J. Lavado, D. Gadaleta, C. Toma, A. Golbamaki, A.A. Toropov, A.P. Toropova, M. Marzo, D. Baderna, E. Benfenati,
Zebrafish AC50 Modelling: (Q)SAR Models to Predict Developmental Toxicity in Zebrafish Embryo.
Ecotoxicology and Environmental Safety, 202 (2020) 110936.

K. Ghosh, B. Bhardwaj, S.A. Amin, T. Jha & S. Gayen,
Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis,
SAR and QSAR in Environmental Research, 31:6, 439-455, 2020. DOI: 10.1080/1062936X.2020.1771769

Kiran Bagri, Ashwani Kumar, Manisha Nimbhal & Parvin Kumar.
Index of ideality of correlation and correlation contradiction index: a confluent perusal on acetylcholinesterase inhibitors,
Molecular Simulation, 46:10, 777-786, 2020. DOI: 10.1080/08927022.2020.1770753

Clark, T.; Hicks, M. G.
Models of Necessity.
Beilstein Arch. 2020, 202077. doi:10.3762/bxiv.2020.77.v1

Ambure, P., Ballesteros, A., Huertas, F., Camilleri, P., Barigye, S. J., & Gozalbes, R.
Development of Generalized QSAR Models for Predicting Cytotoxicity and Genotoxicity of Metal Oxides Nanoparticles.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), (2020), 5(4), 15-32. doi:10.4018/IJQSPR.20201001.oa2

Jordan P. Lightstone, Lihua Chen, Chiho Kim, Rohit Batra, and Rampi Ramprasad,
Refractive index prediction models for polymers using machine learning.
Journal of Applied Physics 2020, 127(21): 215105. DOI: 10.1063/5.0008026

Alla P. Toropova and Andrey A. Toropov,
Fullerenes C₆₀ and C₇₀: a model for solubility by applying the correlation intensity index.
Fullerenes, Nanotubes and Carbon Nanostructures, 2020, 28:11, 900-906. DOI:10.1080/1536383X.2020.1779705

Jiakai Cao,Yong Pan, Yanting Jiang, Ronghua Qi, Beilei Yuan, Zhenhua Jia, Juncheng Jiang, and Qingsheng Wang,
Computer-aided Nanotoxicology: Risk Assessment of Metal Oxide Nanoparticles via nano-QSAR.
Green Chem., 2020, 22, 3512-3521. https://doi.org/10.1039/D0GC00933D

Malik, A.A., Phanus-umporn, C., Schaduangrat, N., Shoombuatong, W., Isarankura-Na-Ayudhya, C., Nantasenamat, C.
HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors.
J. Comput. Chem. 2020; 1– 15. https://doi.org/10.1002/jcc.26223

Andrey A. Toropov and Alla P. Toropova,
Correlation Intensity Index: building up models for mutagenicity of silver nanoparticles.
Science of the Total Environment 737 (2020) 139720. https://doi.org/10.1016/j.scitotenv.2020.139720

K. Jafari, M.H. Fatemi, A.P. Toropova, A.A. Toropov,
Correlation Intensity Index (CII) as a criterion of predictive potential: applying to model thermal conductivity of metal oxide-based ethylene glycol nanofluids.
Chemical Physics Letters 754 (2020) 137614. DOI: 10.1016/j.cplett.2020.137614

L. Ly Pham, S. Watford, P. Pradeep, M.T. Martin, R. Thomas, R. Judson, R. Woodrow Setzer, K.P. Friedman,
Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels.
Computational Toxicology, 15, 2020, 100126. https://doi.org/10.1016/j.comtox.2020.100126

M. Zivkovic, M. Zlatanovic, N. Zlatanovic, J. Djordjevic Jocic, M. Golubović, A.M. Veselinović,
Development of novel therapeutics for the treatment of glaucoma based on actin-binding kinase inhibition – in silico approach.
New J. Chem., 2020, 44, 6923-6931.

Yan, X., Sedykh, A., Wang, W., Yan, B., Zhu, H.,
Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations.
Nat. Commun. 11, 2519 (2020). https://doi.org/10.1038/s41467-020-16413-3

Lee, M.-H.; Ta, G.H.; Weng, C.-F.; Leong, M.K.
In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.
Int. J. Mol. Sci. 2020, 21, 3582.

Wang YW, Huang L, Jiang SW, Li K, Zou J, Yang SY.
CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens.
Food and Chemical Toxicology 2020; 135: 110921. DOI: 10.1016/j.fct.2019.110921

N. Nabipour, R. Daneshfar, O. Rezvanjou, M. Mohammadi-Khanaposhtani, A. Baghban, Q. Xiong, L.K.B. Li, S. Habibzadeh, M.H. Doranehgardg,
Estimating biofuel density via a soft computing approach based on intermolecular interactions.
Renewable Energy, Volume 152, 2020, 1086-1098. https://doi.org/10.1016/j.renene.2020.01.140

V.H.J. Mendes dos Santos, D. Pontin, R. Scheibler Rambo, M. Seferin,
The Application of Quantitative Structure–Property Relationship Modeling and Exploratory Analysis to Screen Catalysts for the Synthesis of Oleochemical Carbonates from CO2 and Bio-Based Epoxides.
J. Am. Oil Chem. Soc. (2020), 97: 817-837. DOI 10.1002/aocs.12361

Ashraf, M. R., Bakhat, H. F., Shah, G. M., Arshad, H. M., Mahmood, Q., Shahid, N.
Role of Hydrophobicity in Bio-Accessibility of Environmental Pollutants Among Different Organisms.
Polish Journal of Environmental Studies. 2020; 29(5): 3509–3516. https://doi.org/10.15244/pjoes/111578

Andrey A. Toropov, Alla P. Toropova, Edoardo Carnesecchi, Emilio Benfenati, Jean Lou Dorne,
The Index of Ideality of Correlation and the variety of molecular rings as a base to improve model of HIV-1 protease inhibitors activity.
Structural Chemistry, (2020) 31: 1441–1448. DOI: 10.1007/s11224-020-01525-9

Katarzyna Stępnik, Wirginia Kukula-Koch,
In Silico Studies on Triterpenoid Saponins Permeation through the Blood-Brain Barrier Combined with Postmortem Research on the Brain Tissues of Mice Affected by Astragaloside IV Administration.
International Journal of Molecular Sciences 2020, 21(7): 2534. DOI: 10.3390/ijms21072534

Wenli Yan, Guimei Lin, Rong Zhang, Zhen Liang, Lixian Wu, Wenjuan Wu,
Studies on molecular mechanism between ACE and inhibitory peptides in different bioactivities by 3D-QSAR and MD simulations.
Journal of Molecular Liquids, 304, 2020, 112702.

Shankar Suman, Ram Singh,
Thiophene-based Schiff base ligand as ionophore for Ni(II)-selective polyvinyl chloride membrane electrode.
Journal of Polymer Engineering, 2020, 40(6), 481-485. https://doi.org/10.1515/polyeng-2019-0325

Ronghua Qi, Yong Pan, Jiakai Cao, Zhenhua Jia, Juncheng Jiang,
The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR.
Chemosphere, 249, 2020, 126175. https://doi.org/10.1016/j.chemosphere.2020.126175

Robert Ancuceanu, Marilena Viorica Hovanet, Adriana Iuliana Anghel, Florentina Furtunescu, Monica Neagu, Carolina Constantin and Mihaela Dinu,
Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.
Int. J. Mol. Sci. 2020, 21, 2114. doi:10.3390/ijms21062114

Bhalerao, A., Sivandzade, F., Archie, S.R., Chowdhury, E.A., Noorani, B., Cucullo, L.,
In vitro modeling of the neurovascular unit: advances in the field.
Fluids Barriers CNS 17, 22 (2020). https://doi.org/10.1186/s12987-020-00183-7

Varsou, D.-D., Afantitis, A., Tsoumanis, A., Papadiamantis, A., Valsami-Jones, E., Lynch, I., Melagraki, G.,
Zeta-Potential Read-Across Model Utilizing Nanodescriptors Extracted via the NanoXtract Image Analysis Tool Available on the Enalos Nanoinformatics Cloud Platform.
Small 2020, 1906588. https://doi.org/10.1002/smll.201906588

Zare-Shahabadi, V., Mahmoodi-Reihani, M., Abbasitabar, F.,Zare-Shahabadi, V.,
In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling.
ACS Omega, 2020, 5, 11, 5951–5958. https://doi.org/10.1021/acsomega.9b04302

Parvin Kumar, Ashwani Kumar,
CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index.
Chemometrics and Intelligent Laboratory Systems, 200, 2020, 103982. https://doi.org/10.1016/j.chemolab.2020.103982

Toropov, A.A.; Toropova, A.P.
QSPR/QSAR: State-of-Art, Weirdness, the Future.
Molecules 2020, 25(6), 1292. https://doi.org/10.3390/molecules25061292

E. Carnesecchi, G. Raitano, A. Gamba, E. Benfenati & A. Roncaglioni,
Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA’s databases,
SAR and QSAR in Environmental Research, 2020, 31:1, 33-48, DOI: 10.1080/1062936X.2019.1690045

Rajesh Kumar Singh, Amit Ranjan, Ruchita Tripathi, Akhileshwar Kumar Srivastava, Monika Singh, Abhishek Kumar, Anil Kumar Singh, Kamal Nayan Dwivedi, Neelam Atri, Santosh Kumar Singh,
Virtual screening of MAP-Tau protein inhibitors from Semecarpus anacardium Linn. leaf extract for cancer prevention.
BioRxiv 2020.01.08.899708; doi: https://doi.org/10.1101/2020.01.08.899708

Cabello, R.S., Vega-Baudrit, J., Zuluaga, R., & Gañán, P.
Statistical Approach to Regulation of Nanotechnology: Need, Advantages and Disadvantages.
Journal of Biomaterials and Nanobiotechnology 2020, 11(1): 14-32. DOI: 10.4236/jbnb.2020.111002

H. Maouz, L. Khaouane, S. Hanini, Y. Ammi, M. Hamadache and M. Laidi,
QSPR Studies of Carbonyl, Hydroxyl, Polyene Indices, and Viscosity Average Molecular Weight of Polymers under Photostabilization Using ANN and MLR Approaches.
Kem. Ind. 69 (1-2) (2020) 1–16. https://doi.org/10.15255/KUI.2019.022

Antreas Afantitis, Georgia Melagraki, Panagiotis Isigonis, Andreas Tsoumanis, Dimitra Danai Varsou, Eugenia Valsami-Jones, Anastasios Papadiamantis, Laura- Jayne. A Ellis, Haralambos Sarimveis, Philip Doganis, Pantelis Karatzas, Periklis Tsiros, Irene Liampa, Vladimir Lobaskin, Dario Greco, Angela Serra, Pia Anneli Sofia Kinaret, Laura Aliisa Saarimäki,Roland Grafström, Pekka Kohonen, Penny Nymark, Egon Willighagen, Tomasz Puzyn, Anna Rybinska-Fryca, Alexander Lyubartsev, Keld Alstrup Jensen, Jan Gerit Brandenburg, Stephen Lofts, Claus Svendsen, Samuel Harrison, Dieter Maier, Kaido Tamm, Jaak Jänes, Lauri Sikk, Maria Dusinska, Eleonora Longhin, Elise Rundén-Pran, Espen Mariussen, Naouale El Yamani, Wolfgang Unger, Jörg Radnik, Alexander Tropsha, Yoram Cohen, Jerzy Leszczynski, Christine Ogilvie Hendren, Mark Wiesner, David Winkler, Noriyuki Suzuki, Tae Hyun Yoon, Jang-Sik Choi, Natasha Sanabria, Mary Gulumian, Iseult Lynch,
NanoSolveIT Project: Driving Nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment.
Computational and Structural Biotechnology Journal, 18, 2020, Pages 583-602. https://doi.org/10.1016/j.csbj.2020.02.023

Irini Furxhi, Finbarr Murphy, Martin Mullins, Athanasios Arvanitis & Craig A. Poland,
Nanotoxicology data for in silico tools: a literature review,
Nanotoxicology, 2020, 14:5, 612-637. DOI: 10.1080/17435390.2020.1729439

Lauro, F.-V., Maria, L.-R., Francisco, D.-C., Marcela, R.-N., Virginia, M.-A., Alejandra, G.-E.E., Hau-Heredia-Lenin, and Yazmin, O.-A.
Design and synthesis of new azetidine-steroid derivative with inotropic activity in a heart failure model.
Vietnam Journal of Chemistry, (2020), 58: 10-19. doi:10.1002/vjch.201900131

Jia, Q., Shi, Q., Yan, F., Wang, Q.
Norm index-based QSPR model for describing the n-octanol/water partition coefficients of organics.
Environ. Sci. Pollut. Res. 27, 15454–15462 (2020). https://doi.org/10.1007/s11356-020-08020-y

Anurag T.K. Baidya, Kalyan Ghosh, Sk. Abdul Amin, Nilanjan Adhikari, Nirmal J, Tarun Jha and Shovanlal Gayen,
In silico modelling, identification of crucial molecular fingerprints, and prediction of new possible substrates of human organic cationic transporters 1 and 2.
New J. Chem., 2020, 44, 4129-4143. https://doi.org/10.1039/C9NJ05825G

Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski,
The index of ideality of correlation: models of flash points of ternary mixtures.
New Journal of Chemistry, 2020, 44, 4858 - 4868. DOI: 10.1039/D0NJ00121J

Alla P. Toropova and Andrey A. Toropov,
Chapter 3. Use of the Monte Carlo Method to Build up QSPR/QSAR Models: Index of Ideality of Correlation and Correlation Intensity Index.
In book: Thomas B. Hall (Editor) Monte Carlo Methods: History and Applications. Series: Mathematics Research Developments.
Nova, 2020. ISBN: 978-1-53617-723-7 https://novapublishers.com/shop/monte-carlo-methods-history-and-applications/

Cash, A.; Theus, M.H.
Mechanisms of Blood–Brain Barrier Dysfunction in Traumatic Brain Injury.
Int. J. Mol. Sci. 2020, 21, 3344.

Ancuceanu, R.; Hovanet, M.V.; Anghel, A.I.; Furtunescu, F.; Neagu, M.; Constantin, C.; Dinu, M.
Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.
Preprints 2020, 2020020178. (doi: 10.20944/preprints202002.0178.v1)

Dörgő, G., Péter Hamadi, O., Varga, T., Abonyi, J.
Mixtures of QSAR models: Learning application domains of pK: predicto rs.
Journal of Chemometrics. 2020; e3223. https://doi.org/10.1002/cem.3223

Chayawan, C.; Toma, C.; Benfenati, E.; Caballero Alfonso, A.Y.
Towards an Understanding of the Mode of Action of Human Aromatase Activity for Azoles through Quantum Chemical Descriptors-Based Regression and Structure Activity Relationship Modeling Analysis.
Molecules 2020, 25, 739.

Jia, Q., Liu, T., Yan, F. and Wang, Q.
Norm Index–Based QSAR Model for Acute Toxicity of Pesticides Toward Rainbow Trout.
Environ. Toxicol. Chem., (2020), 39: 352-358. doi:10.1002/etc.4621

Jafari, K. & Fatemi, M.H.
Application of nano-quantitative structure–property relationship paradigm to develop predictive models for thermal conductivity of metal oxide-based ethylene glycol nanofluids.
J. Therm. Anal. Calorim. 142, pages 1335–1344(2020). https://doi.org/10.1007/s10973-019-09215-3

Alla P. Toropova, Andrey A. Toropov, Edoardo Carnesecchi, Emilio Benfenati, Jean Lou Dorne,
The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides.
Environmental Science and Pollution Research, 27, pages 13339–13347 (2020). DOI: 10.1007/s11356-020-07820-6

H. Maouz, L. Khaouane, S. Hanini, Y. Ammi, M. Hamadache and M. Laidi,
QSPR Studies of Carbonyl, Hydroxyl, Polyene Indices, and Viscosity Average Molecular Weight of Polymers under Photostabilization Using ANN and MLR Approaches.
Kem. Ind. 69 (1-2) (2020) 1–16. https://doi.org/10.15255/KUI.2019.022

Nymark, P., Bakker, M., Dekkers, S., Franken, R., Fransman, W., García-Bilbao, A., Greco, D., Gulumian, M., Hadrup, N., Halappanavar, S., Hongisto, V., Hougaard, K. S., Jensen, K. A., Kohonen, P., Koivisto, A. J., Dal, M., Oosterwijk, T., Poikkimäki, M., Rodriguez-Llopis, I., Stierum, R., Sørli, J. B., Grafström, R.,
Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices.
Small 2020, 1904749. https://doi.org/10.1002/smll.201904749

Furxhi, I.; Murphy, F.; Mullins, M.; Arvanitis, A.; Poland, C.A.
Practices and Trends of Machine Learning Application in Nanotoxicology.
Nanomaterials 2020, 10, 116. https://doi.org/10.3390/nano10010116

M.Marzo, G.J. Lavado, F. Como, A.A. Toropov, A.P. Toropova, D. Baderna, C. Cappelli, A. Lombardo, C. Toma, M. Blázquez Sánchez, E. Benfenati,
QSAR models for Biocides. The example of the prediction of Daphnia Magna acute toxicity.
SAR and QSAR in Environmental Research, 31:3, 227-243, 2020.

Wang Y., Liu H., Yang X.,
Development of quantitative structure-property relationship model for predicting the field sampling rate (Rs) of Chemcatcher passive sampler.
Environ. Sci. Pollut. Res. Int., 2020, 27(10): 10415-10424. DOI: 10.1007/s11356-020-07616-8

E. Benfenati, A. Lombardo,
VEGAHUB for Ecotoxicological QSAR Modeling.
In book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY,(2020) pp.759-787. DOI: 10.1007/978-1-0716-0150-1_30

Aros, D.; Garrido, N.; Rivas, C.; Medel, M.; Müller, C.; Rogers, H.; Úbeda, C.
Floral Scent Evaluation of Three Cut Flowers Through Sensorial and Gas Chromatography Analysis.
Agronomy 2020, 10, 131. https://doi.org/10.3390/agronomy10010131

Ambure P., Cordeiro M.N.D.S.
Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Datasets.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp. 97-109. https://doi.org/10.1007/978-1-0716-0150-1_5

Gini G., Zanoli F.
Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp. 111-149. https://doi.org/10.1007/978-1-0716-0150-1_6

Varsou DD., Tsoumanis A., Afantitis A., Melagraki G.
Enalos Cloud Platform: Nanoinformatics and Cheminformatics Tools.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp.789-800. https://doi.org/10.1007/978-1-0716-0150-1_31

Speck-Planche A.
Multi-scale QSAR Approach for Simultaneous Modeling of Ecotoxic Effects of Pesticides.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp.639-660. https://doi.org/10.1007/978-1-0716-0150-1_26

Hamadache M., Benkortbi O., Amrane A., Hanini S.
QSAR Approaches and Ecotoxicological Risk Assessment.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp. 615-638. https://doi.org/10.1007/978-1-0716-0150-1_25

Chang C.M., Chang CW., Wu FW., Chang L., Liu TC.
In Silico Ecotoxicological Modeling of Pesticide Metabolites and Mixtures.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp.561-589. https://doi.org/10.1007/978-1-0716-0150-1_23

Ojha P.K., Mandal D., Roy K.
QSPR Modeling of Adsorption of Pollutants by Carbon Nanotubes (CNTs).
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp.477-511. https://doi.org/10.1007/978-1-0716-0150-1_20

Khan K., Sanderson H., Roy K.
Ecotoxicological QSARs of Personal Care Products and Biocides.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp.357-386. https://doi.org/10.1007/978-1-0716-0150-1_16

Rasulev B.
Ecotoxicological QSAR Modeling of Nanomaterials: Methods in 3D-QSARs and Combined Docking Studies for Carbon Nanostructures.
In Book: Roy K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY, (2020) pp.215-233. https://doi.org/10.1007/978-1-0716-0150-1_10

P.R. Duchowicz,
QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides,
SAR and QSAR in Environmental Research, 2020, 31:2, 135-148, DOI: 10.1080/1062936X.2019.1699602

Stolbov, L.A.; Druzhilovskiy, D.S.; Filimonov, D.A.; Nicklaus, M.C.; Poroikov, V.V.
(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds.
Molecules 2020, 25, 87. https://doi.org/10.3390/molecules25010087

Samir Chtita, Adnane Aouidate, Assia Belhassan, Abdellah Ousaa, Abdelali Idrissi Taourati, Bouhya Elidrissi, Mounir Ghamali, Mohammed Bouachrine and Tahar Lakhlifi,
QSAR study of N-substituted Oseltamivir derivatives as potent avian influenza virus H5N1 inhibitors using quantum chemical descriptors and statistical methods.
New J. Chem., 2020,44, 1747-1760. DOI:10.1039/C9NJ04909F

Bhardwaj, M., Masand, N., Sahoo, J., & Patil, V. M.
Risk Assessment of Cosmetic Preservatives Using QSAR: QSAR of Cosmetic Preservatives.
International Journal of Quantitative Structure-Property Relationships (IJQSPR)5(1), 2020, 44-62. DOI: 10.4018/IJQSPR.2020010103

Dipayan Mondal, Kalyan Ghosh, Anurag T. K. Baidya, Anindia Mondal Gantait & Shovanlal Gayen,
Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of Nitroaromatics,
Toxicology Mechanisms and Methods, 2020, 30:4, 257-265. DOI: 10.1080/15376516.2019.1709238

Shahin Ahmadi,
Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria.
Chemosphere, 242, 2020, 125192. https://doi.org/10.1016/j.chemosphere.2019.125192

Villaverde J.J., Sevilla-Morán B., López-Goti C., Alonso-Prados J.L., Sandín-España P.
Contributions of Computer-Based Chemical Modeling Technologies on the Risk Assessment and the Environmental Fate Study of (Nano)pesticides.
In book: Shukla V., Kumar N. (eds), Environmental Concerns and Sustainable Development. Springer, Singapore. 2020, Volume 1: Air, Water and Energy Resources, Chapter 1, pp 1-27. DOI: 10.1007/978-981-13-5889-0_1

Toropova, A. P., Toropov, A. A., & Benfenati, E.
QSAR-Models, Validation, and IIC-Paradox for Drug Toxicity.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), 5(1), 2020, 22-43. DOI:10.4018/IJQSPR.2020010102

Rasulev, B., & Casanola-Martin, G.
QSAR/QSPR in Polymers: Recent Developments in Property Modeling.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), 5(1), (2020), 80-88. DOI:10.4018/IJQSPR.2020010105

Andrey A.Toropov, Alla P.Toropova, Marco Marzo, Emilio Benfenati,
Use of the index of ideality of correlation to improve aquatic solubility model.
Journal of Molecular Graphics and Modelling 96 (2020) 107525.

E. Carnesecchi, A.A. Toropov, A.P. Toropova, N. Kramer, C.Svendsen, J.L. Dorne, E. Benfenati,
Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models.
Science of The Total Environment, 704 (2020) 135302.

A.P. Toropova, P.R. Duchowicz, L.M. Saavedra, E.A. Castro and A.A. Toropov,
The use of the index of ideality of correlation to build up models for bioconcentration factor.
Molecular Informatics, 2020, 39, 1900070. https://doi.org/10.1007/s11224-019-01468-w

Fiorella Cravero, Santiago A. Schustik, M. Jimena Martínez, Gustavo E. Vázquez, Mónica F. Díaz, Ignacio Ponzoni,
Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RVDD Algorithm Using Synthetic Polydisperse Data Sets.
J. Chem. Inf. Model. 2020, 60, 2, 592–603. https://doi.org/10.1021/acs.jcim.9b00867

Baderna D., Gadaleta D., Lostaglio E., Selvestrel G., Raitano G.,Lombardo A., Benfenati E.,
New in silico models to predict in vitro micronucleus induction as marker of genotoxicity,
Journal of Hazardous Materials 385, 2020, 121638.

Parvin Kumar & Ashwani Kumar.
Nucleobase Sequence Based Building up of Reliable QSAR Models with The Index of Ideality Correlation using Monte Carlo Method.
Journal of Biomolecular Structure and Dynamics, 38:11, 3296-3306, 2020. DOI: 10.1080/07391102.2019.1656109

Alla P. Toropova, Andrey A. Toropov, Edoardo Carnesecchi, Emilio Benfenati, Jean Lou Dorne.
The index of ideality of correlation: models for lammability of binary liquid mixtures.
Chemical Papers, 2020, 74(2): 601-609. https://doi.org/10.1007/s11696-019-00903-w

Tomislav Kostić, Marina Deljanin Ilić, Zoran Perišić, Dragan Milić, Miodrag Đorđević, Mladjan Golubović, Goran Koraćević, Sonja Šalinger Martinović, Snežana Ćirić Zdravković, Saša Živić, Milan Lazarević, Dragana Stanojević, Sonja Dakić, Jelena Lilić & Aleksandar Veselinović.
Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - in silico approach,
Journal of Biomolecular Structure and Dynamics, 2020, 38:8, 2304-2313. DOI: 10.1080/07391102.2019.1630319

Vanja P. Ničković, Nebojša R. Mitić, Biljana D. Krdžić, Jelena D. Krdžić, Gordana R. Nikolić, Maja Z. Vasić, Goran Ranković, Petar Babović, Dušan Sokolović & Aleksandar M. Veselinović
Design and development of novel therapeutics for brucellosis treatment based on carbonic anhydrase inhibition,
Journal of Biomolecular Structure and Dynamics, Volume 38, Issue 6, 2020, Pages 1848-1857. DOI: 10.1080/07391102.2019.1619626

Andrey A. Toropov, Alla P. Toropova,
The Monte Carlo Method as a tool to build up predictive QSPR/QSAR.
Curr. Comput. Aided Drug Des. 2020, 16(3), 197 – 206. DOI: 10.2174/1573409915666190328123112

• TO THE BEGINNING

2019

Scotti L., Scotti M.T.
Medicinal Chemistry Studies Applied to Protein Targets.
Curr. Protein Pept. Sci. 2019; 20(12):1132–1134. doi:10.2174/138920372012191114113702

Saxena D., Sharma A., Siddiqui M.H., Kumar R.
Blood Brain Barrier Permeability Prediction Using Machine Learning Techniques: An Update.
Curr. Pharm. Biotechnol. 2019; 20(14): 1163-1171.

Gupta V.K., Rana P.S.
Activity Assessment of Small Drug Molecules in Estrogen Receptor using Multilevel Prediction Model.
IET Syst. Biol. 2019; 13(3): 147-158.

Vishan Kumar Gupta, Prashant Singh Rana,
Toxicity prediction of small drug molecules of aryl hydrocarbon receptor using proposed ensemble model.
Turkish Journal of Electrical Engineering and Computer Sciences 27(4) (2019), 2833-2849. DOI: 10.3906/elk-1809-9

Zivkovic, M., Zlatanovic, M., Zlatanović, N., Golubović, M., Veselinović, A.M.
Development of novel therapeutics for glaucoma filtration surgery based on transforming growth factor-ß receptor 1 inhibition.
New J. Chem., 2019,43, 19265-19273. http://dx.doi.org/10.1039/C9NJ05393J

Willie Peijnenburg, Guangchao Chen, and Vijver Martina, Nano-QSAR for Environmental Hazard Assessment: Turning Challenges into Opportunities.
In book: A. Gajewicz, T. Puzyn (Eds) Computational Nanotoxicology: Challenges and Perspectives.
Singapore: Jenny Stanford Publishing, Published December 26, 2019. Chapter 7, pp.303. DOI: 10.1201/9780429341373-7

E. Wyrzykowska, K. Jagiello, B. Rasulev, T. Puzyn, Descriptors in Nano-QSAR/QSPR Modeling.
In book: A. Gajewicz, T. Puzyn (Eds) Computational Nanotoxicology: Challenges and Perspectives.
Singapore: Jenny Stanford Publishing, Published December 26, 2019. Chapter 6, pp.245-302. DOI: 10.1201/9780429341373-6

Singh, K. ; Yu, Q. ; Dasgupta, S. ; Gompper, G.; Auth, T.
From Modeling Nanoparticle–Membrane Interactions toward Nanotoxicology.
In book: A. Gajewicz, T. Puzyn (Eds) Computational Nanotoxicology: Challenges and Perspectives.
Singapore: Jenny Stanford Publishing, Published December 26, 2019. Chapter 5, pp. 217-244.

Gini G, Ferrari T, Lombardo A, Cassano A, Benfenati E.,
A New QSAR Model for Acute Fish Toxicity based on Mined Structural Alerts.
J. Toxicol. Risk Assess. (2019) 5:016. doi. org/10.23937/2572-4061.1510016

Li, C.; Huang, G.; Cheng, G.; Zheng, M.; Zhou, N.
Nanomaterials in the Environment: Research Hotspots and Trends.
Int. J. Environ. Res. Public Health 2019, 16(24), 5138. https://doi.org/10.3390/ijerph16245138

Buglak, A.A.; Zherdev, A.V.; Dzantiev, B.B.
Nano-(Q)SAR for Cytotoxicity Prediction of Engineered Nanomaterials.
Molecules 2019, 24, 4537. https://doi.org/10.3390/molecules24244537

S. Goicoechea, M. L. Sbaraglini, S. R. Chuguransky, J. F. Morales, M. E. Ruiz, A. Talevi, C. L. Bellera,
Application of Machine Learning Approaches to Identify New Anticonvulsant Compounds Active in the 6 Hz Seizure Model,
In: Cota V., Barone D., Dias D., Damázio L. (eds) Computational Neuroscience. LAWCN 2019. Communications in Computer and Information Science, vol 1068, pp.3-19. Springer, Cham. https://doi.org/10.1007/978-3-030-36636-0_1

Ghasem Ghasemi,
A QSAR Study on the Biological Activities of Polyamines as Anti-alzheimer Drugs by Monte Carlo Optimization.
Journal of Scientific & Industrial Research, Vol. 78, 2019, pp. 323-327. http://nopr.niscair.res.in/handle/123456789/47149

E. Carnesecchi, G. Raitano, A. Gamba, E. Benfenati & A. Roncaglioni.
Evaluation of non-commercial models for genotoxicity and carcinogenicity in the assessment of EFSA’s databases.
SAR and QSAR in Environmental Research, (2020) 31:1, 33-48. DOI: 10.1080/1062936X.2019.1690045

Edoardo Carnesecchi, Claus Svendsen, Stefano Lasagni, Audrey Grech, Nadia Quignot, Billy Amzal, Cosimo Toma, Simone Tosi, Agnes Rortais, Jose Cortinas-Abrahantes, Ettore Capri, Nynke Kramer, Emilio Benfenati, David Spurgeon, Gilles Guillot, Jean Lou Christian Michel Dorne,
Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment.
Environment International 133 (2019) 105256.

Alla P. Toropova and Andrey A. Toropov,
Whether the Validation of the Predictive Potential of Toxicity Models is Solved Task?
Current Topics in Medicinal Chemistry, 2019; 19(29): 2643 - 2657. DOI: 10.2174/1568026619666191105111817

Gerardo M. Casañola-Martin, and Hai Pham-The.
Machine Learning Applications in Nanomedicine and Nanotoxicology: An Overview.
International Journal of Applied Nanotechnology Research (IJANR) 4(1) 2019, 1-7. DOI: 10.4018/IJANR.2019010101

Juan J.Torres, Cristhian D.Tinjaca, Oscar A.Alvarez, Jorge M.Gómez,
Optimization Proposal for Emulsions Formulation Considering a Multiscale Approach.
Chemical Engineering Science, Volume 212, February 2020, 115326. https://doi.org/10.1016/j.ces.2019.115326

Ana S. Moura, Amit K. Halder, and M. Natália D.S. Cordeiro,
From biomedicinal to in silico models and back to therapeutics: a review on the advancement of peptidic modeling.
Future Medicinal Chemistry 2019, 11:17, 2313-2331. https://doi.org/10.4155/fmc-2018-0365

X Raichel Nivetha, M Jeslin Jeba Soundari, MSA Muthukumar Nadar, D Premnath, Paulraj Mosae Selvakumar, and Ruey-yi Chang;
An Insight into Cancer and Anticancer Drugs.
Acta Scientific Medical Sciences 3(8) (2019), 32-43.

Khalid Bouhedjar, Abdelmalek Khorief Nacereddine, Hamida Ghorab, Abdelhafid Djerourou,
QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), 4(4), 2019, pages 15-26. DOI: 10.4018/IJQSPR.2019100102

Wilm, A.; Stork, C.; Bauer, C.; Schepky, A.; Kühnl, J.; Kirchmair, J.
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability.
Int. J. Mol. Sci. 2019, 20, 4833.

Sason, H. and Shamay, Y.,
Nanoinformatics in Drug Delivery.
Isr. J. Chem. 2019, 59, 1–11. https://doi.org/10.1002/ijch.201900042

Silvina E. Fioressi, Daniel E. Bacelo, Pablo R. Duchowicz,
QSAR study of human epidermal growth factor receptor (EGFR) inhibitors: conformation-independent models.
Medicinal Chemistry Research, 2019, Volume 28, Issue 11, pp. 2079–2087. https://doi.org/10.1007/s00044-019-02437-y

Shin, J. H., Lee, B. H. and Lee, S. K.,
Development of QSAR Model for Subchronic Inhalation Toxicity Using Random Forest Regression Method.
Bulletin of the Korean Chemical Society 40(8), 2019, 819-825. DOI: 10.1002/bkcs.11835

Chakravarti Suman K., Alla Sai Radha Mani,
Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks.
Front. Artif. Intell., 2, 2019, 17. https://doi.org/10.3389/frai.2019.00017

Alberca Lucas N., Chuguransky Sara R., Álvarez Cora L., Talevi Alan, Salas-Sarduy Emir.
In silico Guided Drug Repurposing: Discovery of New Competitive and Non-competitive Inhibitors of Falcipain-2.
Frontiers in Chemistry, 7 (2019) 534. DOI:10.3389/fchem.2019.00534

Speck-Planche A.
Multiple Perspectives in Anti-cancer Drug Discovery: From old Targets and Natural Products to Innovative Computational Approaches.
Anticancer Agents Med Chem. 2019; 19(2): 146-147. doi: 10.2174/187152061902190418105054

David van der Spoel, Sergio Manzetti, Haiyang Zhang, Andreas Klamt.
Prediction of Partition Coefficients of Environmental Toxins Using Computational Chemistry Methods.
ACS Omega 2019, 4, 9, 13772-13781. doi: 10.1021/acsomega.9b01277

Janvhi Machhar, Ansh Mittal, Surendra Agrawal, Anil M. Pethe, Prashant S. Kharkar.
Computational prediction of toxicity of small organic molecules: state-of-the-art.
Physical Sciences Reviews, 4(10), 2019. https://doi.org/10.1515/psr-2019-0009

S. Takano, H. Kaneko.
Monomer Design of Polymer Materials with High RefractiveIndex and High Glass Transition Temperature.
Journal of Computer Chemistry Japan, 2019, 18(2): 115-121. DOI: 10.2477/jccj.2019-0004

Gini G., Zanoli F., Gamba A., Raitano G., Benfenati E.
Could deep learning in neural networks improve the QSAR models?
SAR QSAR Environ. Res. 2019 Aug 28: 1-26. doi: 10.1080/1062936X.2019

Przybyłek, M., Studziński, W., Gackowska, A., Gaca, J.
The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification.
Environ. Sci. Pollut. Res. 2019, Volume 26, Issue 27, pp 28188–28201. https://doi.org/10.1007/s11356-019-05968-4

V. Forest, J.-F. Hochepied, J. Pourchez,
Importance of choosing relevant biological endpoints to predict nanoparticle toxicity with computational approaches for human health risk assessment.
Chemical Research in Toxicology, 2019, 32, 7, 1320-1326.

Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati,
QSPR as a random event: solubility of fullerenes C[60] and C[70].
Fullerenes, Nanotubes and Carbon Nanostructures, 2019, 27(10), 816-821. DOI: 10.1080/1536383X.2019.1649659

P. Kumar, A. Kumar & J. Sindhu.
In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method,
SAR and QSAR in Environmental Research, 2019, 30:8, 525-541. DOI: 10.1080/1062936X.2019.1629998

González-Durruthy M., Scanavachi G., Rial R., Liu Z., Cordeiro M.N.D.S., Itri R., Ruso J.M.,
Structural and energetic evolution of fibrinogen toward to the betablocker interactions.
Int. J. Biol. Macromol. 2019; 137: 405-419.

Piotr Cysewski and Maciej Przybyłek,
Predicting Value of Binding Constants of Organic Ligands to Beta-Cyclodextrin: Application of MARSplines and Descriptors Encoded in SMILES String.
Symmetry 2019, 11(7), 922. https://doi.org/10.3390/sym11070922

F. Lunghini, G. Marcou, P. Azam, R. Patoux, M.H. Enrici, F. Bonachera, D. Horvath & A. Varnek.
QSPR models for bioconcentration factor (BCF): are they able to predict data of industrial interest?
SAR and QSAR in Environmental Research, 30:7, (2019) 507-524.

Piotr Cysewski.
Application of the Consonance Solvent Concept for Accurate Prediction of Buckminster Solubility in 180 Net Solvents using COSMO-RS Approach.
Symmetry 2019, 11, 828. doi:10.3390/sym11060828

Abdel-Aal, M.A.A., Abdel-Aziz, S.A., Shaykoon, M.S.A., Abuo-Rahma, G.E.-D.A.
Towards anticancer fluoroquinolones: A review article.
Arch. Pharm. Chem. Life Sci. 2019; e1800376. https://doi.org/10.1002/ardp.201800376

Pawar Gopal, Madden Judith C., Ebbrell David, Firman James W., Cronin Mark T. D.
In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR
Frontiers in Pharmacology, 10, 2019, 561. DOI=10.3389/fphar.2019.00561

De, P., Bhattacharyya, D. & Roy, K.
Application of multilayered strategy for variable selection in QSAR modeling of PET and SPECT imaging agents as diagnostic agents for Alzheimer's disease.
Struct. Chem. 2019, Volume 30, Issue 6, pp. 2429–2445. https://doi.org/10.1007/s11224-019-01376-z

Pereira, R.R.; Testi, M.; Rossi, F.; Silva Junior, J.O.C.; Ribeiro-Costa, R.M.; Bettini, R.; Santi, P.; Padula, C.; Sonvico, F.
Ucuùba (Virola surinamensis) Fat-Based Nanostructured Lipid Carriers for Nail Drug Delivery of Ketoconazole: Development and Optimization Using Box-Behnken Design.
Pharmaceutics 2019, 11, 284.

Khan, K., Khan, P.M., Lavado, G., Valsecchi, C., Pasqualini, J., Baderna, D., Marzo, M., Lombardo, A., Roy, K., Benfenati, E.
QSAR modeling of Daphnia magna and fish toxicities of biocides using 2D descriptors
Chemosphere,229 (2019) pp. 8-17.

Golbraikh, A.
Value of p-Value.
Mol. Inf. 2019, 38, 1800152. https://doi.org/10.1002/minf.201800152

Ponikvar-Svet, M., Zeiger, D.N. & Liebman, J.F.
Interplay of thermochemistry and Structural Chemistry, the journal (volume 29, 2018, issues 3–4) and the discipline.
Struct Chem., 30 (4), 2019, 1517-1526. https://doi.org/10.1007/s11224-019-01359-0

Masand, V. H., Elsayed, N. N., Thakur, S. D., Gawhale, N. and Rathore, M. M.,
Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D-QSAR Analysis.
Mol. Inf., 2019, 38, 1800149. doi:10.1002/minf.201800149

Ahmadi, S., Mehrabi, M., Rezaei, S., Mardafkan, N.
Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals
Journal of Molecular Structure, 1191, (2019) 165-174.

Vishnepolsky, B.; Zaalishvili, G.; Karapetian, M.; Nasrashvili, T.; Kuljanishvili, N.; Gabrielian, A.; Rosenthal, A.; Hurt, D.E.; Tartakovsky, M.; Grigolava, M.; Pirtskhalava, M.
De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria.
Pharmaceuticals 2019, 12(2), 82.

G.A. Kpotin, A.L. Bédé, A. Houngue-Kpota, W. Anatovi, U.A. Kuevi, G.S. Atohoun, J.-B. Mensah, J.S. Gómez-Jeria, M. Badawi.
Relationship between electronic structures and antiplasmodial activities of xanthone derivatives: a 2D-QSAR approach.
Struct. Chem., 2019, Volume 30, Issue 6, pp. 2301–2310. https://doi.org/10.1007/s11224-019-01333-w

Doucet, J. P., Doucet-Panaye, A. and Papa, E.,
Topological QSAR modelling of carboxamides repellent activity to Aedes aegypti.
Mol. Inf. 2019, 38, 1900029. https://doi.org/10.1002/minf.201900029

Gopal Pawar, Judith Madden, David Ebbrell, James Firman, Mark Cronin,
In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR.
Front. Pharmacol. 10, 2019, 561. doi: 10.3389/fphar.2019.00561

K. Khan, D. Baderna, C. Cappelli, C. Toma, A. Lombardo, K. Roy, E. Benfenati,
Ecotoxicological QSAR modeling of organic compounds against fish: Application of fragment based descriptors in feature analysis.
Aquatic Toxicology 212 (2019) 162–174.

Andrey A. Toropov, Alla P. Toropova,
QSAR as a random event: criteria of predictive potential for a chance model.
Structural Chemistry, 30(5), 2019, 1677-1683. DOI: 10.1007/s11224-019-01361-6

Andrey A. Toropov, Alla P. Toropova,
The Correlation Contradictions Index (CCI): building up reliable models of mutagenic potential of silver nanoparticles under different conditions using quasi-SMILES.
Science of The Total Environment, 681 (2019) 102-109.

L.G. Soeteman-Hernández, C. Bekker, M. Groenewold, P.A. Jantunen, A. Mech, K. Rasmussen, J. Riego Sintes, A.J.A.M. Sips, C.W. Noorlander.
Perspective on how regulators can keep pace with innovation: Outcomes of a European Regulatory Preparedness Workshop on nanomaterials and nano- enabled products.
NanoImpact, 14, 2019, 100166.

P. Gong, S. Thangapandian, Y. Li, G. Idakwo, J. Luttrell IV, M. Chen, H. Hong, C. Zhang,
Mode-of-Action-Guided, Molecular Modeling-Based Toxicity Prediction: A Novel Approach for In Silico Predictive Toxicology.
In book: Advances in Computational Toxicology, Challenges and Advances in Computational Chemistry and Physics, Hong H. (eds), vol 30, 2019, pp. 99-118. Springer, Cham. https://doi.org/10.1007/978-3-030-16443-0_6

E. Benfenati, A. Roncaglioni, A. Lombardo, A. Manganaro,
Integrating QSAR, Read-Across, and Screening Tools: The VEGAHUB Platform as an Example.
In book: Advances in Computational Toxicology. Challenges and Advances in Computational Chemistry and Physics, Hong H. (eds), vol 30, 2019, pp. 365-381. Springer, Cham. https://doi.org/10.1007/978-3-030-16443-0_18

Ran Su, Xinyi Liu, Guobao Xiao, Leyi Wei,
Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction,
Briefings in Bioinformatics, 2019, bbz022, https://doi.org/10.1093/bib/bbz022

Andrey A. Toropov, Alla P. Toropova, Gianluca Selvestrel, Emilio Benfenati,
Idealization of correlations between optimal SMILES-based descriptors and skin sensitization.
SAR and QSAR in Environmental Research, 30(6), 2019, 447-455.

Xiliang Yan, Alexander Sedykh, Wenyi Wang, Xiaoli Zhao, Bing Yan and Hao Zhu,
In silico profiling nanoparticles: predictive nanomodeling using universal nanodescriptors and various machine learning approaches.
Nanoscale, 2019, 11, 8352-8362.

Lykhmus O., Koval L., Voytenko L., Uspenska K., Komisarenko S., Deryabina O., Shuvalova N., Kordium V., Ustymenko A., Kyryk V. and Skok M.,
Intravenously Injected Mesenchymal Stem Cells Penetrate the Brain and Treat Inflammation-Induced Brain Damage and Memory Impairment in Mice.
Frontiers in Pharmacology, 10, 2019, 355.

Ponikvar-Svet, M., Zeiger, D.N. & Liebman, J.F.,
Interplay of thermochemistry and Structural Chemistry: the journal (volume 29, 2018, issues 1–2) and the discipline.
Struct Chem (2019) pp. 1–11. https://doi.org/10.1007/s11224-019-01344-7

Romano J.D., Tatonetti N.P.,
Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives.
Frontiers in Genetics, 2019, 10, 368. https://doi.org/10.3389/fgene.2019.00368

A.A. Toropov, A.P. Toropova, D. Leszczynska, J. Leszczynski,
"Ideal correlations" for biological activity of peptides.
BioSystems,181 (2019) 51-57. https://doi.org/10.1016/j.biosystems.2019.04.008

Nava Lara, R.A.; Aguilera-Mendoza, L.; Brizuela, C.A.; Peña, A.; Del Rio, G.
Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs.
Molecules 2019, 24, 1258.

Joyita Roy, Probir Kumar Ojha & Kunal Roy,
Risk assessment of heterogeneous TiO₂-based engineered nanoparticles (NPs): a QSTR approach using simple periodic table based descriptors,
Nanotoxicology, 2019; 13(5): 701-716. DOI: 10.1080/17435390.2019.1593543

Jafari S, Shayanfar A.
Modeling to predict the cytotoxicity of SiO₂ and TiO₂ nanoparticles.
J. Res. Pharm. 2019; 23 (2): 267-274.

Bil, A.,
The mechanism of ozonolysis on the surface of C₇₀ fullerene. The free energy surface theoretical study.
Journal of Molecular Structure, 1185,(2019) pp. 361-368.

P.G.R. Achary, A. P.Toropova, A.A.Toropov,
Combinations of graph invariants and attributes of simplified molecular input-line entry system (SMILES)to build up models for sweetness.
Food Research International 122 (2019) 40–46.

Guerreiro, D., Mbemya, G., Bruno, J., Faustino, L., De Figueiredo, J., & Rodrigues, A.
In vitro culture systems as an alternative for female reproductive toxicology studies.
Zygote, 27(2), 2019, 55-63. doi:10.1017/S0967199419000042

Kar, S.; Leszczynski, J.
Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures.
Toxics 2019, 7, 15.

Asif, F , Zahid, Z , Zafar, S , R. Farahani, M , Gao, W .
On topological properties of some convex polytopes by using line operator on their subdivisions.
Hacettepe Journal of Mathematics and Statistics, (2019): 1-10. http://dergipark.gov.tr/hujms/issue/42398/535415

A.A. Toropov, A.P. Toropova, E. Benfenati,
The Index of Ideality of Correlation: QSAR model of acute toxicity for zebrafish (Danio rerio) embryo.
International Journal of Environmental Research (2019) 13:387–394.

Hwanho Choi, Hongsuk Kang, Kee-Choo Chung, and Hwangseo Park,
Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties.
Phys. Chem. Chem. Phys., 2019, 21, 5189-5199.

Paul J. Van den Brink, Sally A. Bracewell, Alex Bush, Anthony Chariton, Catherine B. Choung, Zacchaeus G. Compson, Katherine A. Dafforn, Kathryn Korbel, David R. Lapen, Mariana Mayer-Pinto, Wendy A. Monk, Allyson L. O'Brien, Natalie K. Rideout, Ralf B. Schäfer, Kizar A. Sumon, Ralf C.M. Verdonschot, Donald J. Baird.
Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems: Results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop.
Science of The Total Environment, 684 (2019) 722-726. https://doi.org/10.1016/j.scitotenv.2019.02.455

Lata, S. and Vikas,
Modeling the Solubility of C70 Fullerenes in Diverse Solvents: Role of Quantum-mechanical Descriptors.
Mol. Inf. 2019, 38, 1800112. doi:10.1002/minf.201800112

Mojtaba Falahati, Farnoosh Attar, Majid Sharifi, Thomas Haertlé, Jean-François Berret, Rizwan Hasan Khan, Ali Akbar Saboury,
A health concern regarding the protein corona, aggregation and disaggregation.
Biochimica et Biophysica Acta (BBA) - General Subjects, Volume 1863, Issue 5, 2019, Pages 971-991. https://doi.org/10.1016/j.bbagen.2019.02.012

Manisha, S. Chauhan, P. Kumar & A. Kumar.
Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method,
SAR and QSAR in Environmental Research, 30:3, 2019, 145-159.

V.E Kuz'min, L.N. Ognichenko, N. Sizochenko, V.A. Chapkin, S.I. Stelmakh, A.O. Shyrykalova, J. Leszczynski,
Combining Features of Metal Oxide Nanoparticles: Nano-QSAR for Cytotoxicity.
International Journal of Quantitative Structure-Property Relationships (IJQSPR) 4(1) 2019, 28-40.

Gini G, Ferrari T, Lombardo A, Cassano A, Benfenati E.
A new QSAR model for acute fish toxicity based on mined structural alerts.
Journal Toxicology Risk Assessment 2019; 5:016.

Floresta, G.; Amata, E.; Gentile, D.; Romeo, G.; Marrazzo, A.; Pittalà, V.; Salerno, L.; Rescifina, A.
Fourfold Filtered Statistical/Computational Approach for the Identification of Imidazole Compounds as HO-1 Inhibitors from Natural Products.
Mar. Drugs 2019, 17, 113.

W. Wang, X. Yan, L. Zhao, D.P. Russo, S. Wang, Y. Liu, A. Sedykh, X. Zhao, B. Yan, H. Zhu,
Universal nanohydrophobicity predictions using virtual nanoparticle library.
Journal of Cheminformatics, 2019, 11:6.

Alla P. Toropova and Andrey A. Toropov,
Applying of the Monte Carlo method for the prediction of behavior of peptides,
Current Protein & Peptide Science (2019) 20: 1. https://doi.org/10.2174/1389203720666190123163907

Alla P. Toropova, Andrey A. Toropov,
Does the index of ideality of correlation detect the better model correctly?
Molecular Informatics, 2019, 38, 1800157. https://doi.org/10.1002/minf.201800157

Prent-Peñaloza L., de la Torre A.F., Velázquez-Libera J.L., Gutiérrez M., Caballero J.,
Synthesis of diN-Substituted Glycyl-Phenylalanine Derivatives by Using Ugi Four Component Reaction and Their Potential as Acetylcholinesterase Inhibitors.
Molecules. 2019; 24(1), 189. doi: 10.3390/molecules24010189

Mariya A. Toropova, Maria Raškova, Ivan Raška Jr., Alla P. Toropova,
The Index of Ideality of Correlation (IIC): model for sweetness.
Monatshefte für Chemie - Chemical Monthly, (2019) 150: 617-623.

Alla P. Toropova, Andrey A. Toropov,
QSPR and nano-QSPR: what is the difference?
Journal of Molecular Structure, 1182 (2019) 141-149.

K. Kümmerer, D.D. Dionysiou, O. Olsson, D. Fatta-Kassinos,
Reducing aquatic micropollutants – Increasing the focus on input prevention and integrated emission management.
Science of The Total Environment, 652, 2019, 836-850.

Andrey A. Toropov, Ivan Raška Jr., Alla P. Toropova, Maria Raškova, Aleksandar M. Veselinović, Jovana B. Veselinović,
The study of the Index of Ideality of Correlation As a new criterion of predictive potential of QSPR/QSAR-models.
Science of the Total Environment 659 (2019) 1387–1394.

S.E. Fioressi, D.E.Bacelo, C. Rojas, J.F. Aranda, P.R.Duchowicz,
Conformation-independent quantitative structure-property relationships study on water solubility of pesticides.
Ecotoxicology and Environmental Safety, 171, 2019, 47-53.

Jose Luis Velazquez Libera, Julio Caballero, Alla Toropova, Andrey Toropov,
Estimation of 2D autocorrelation descriptors and 2D Monte Carlo descriptors as a tool to build up predictive models for acetylcholinesterase (AChE) inhibitory activity.
Chemometrics and Intelligent Laboratory Systems, 184 (2019) 14-21.

Fathi-Vajargah, B. & Hassanzadeh, Z.
Improvements on the hybrid Monte Carlo algorithms for matrix computations.
Sādhanā (2019) 44: 1. https://doi.org/10.1007/s12046-018-0983-y

Jang-Sik Choi, Tung X. Trinh, Tae-Hyun Yoon, Jongwoon Kim, Hyung-Gi Byun,
Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials.
Chemosphere 217 (2019) 243-249.

Ashraf M. R., Bakhat H. F., Shah G. M., Arshad H. M., Mahmood Q., Shahid N.
The Role of Hydrophobicity in Bio-Accessibility of Environmental Pollutants Among Different Organisms.
Pol. J. Environ. Stud. Vol. 28, No. 2 (2019), 1-7.

Kümmerer, K., Dionysiou, D.D., Olsson, O., Fatta-Kassinos, D.
Reducing aquatic micropollutants – Increasing the focus on input prevention and integrated emission management.
(2019) Science of the Total Environment, 652, pp. 836-850.

Masand, V.H., El-Sayed, N.N.E., Bambole, M.U., Patil, V.R., Thakur, S.D.
Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors.
(2019) Journal of Molecular Structure, 1175, pp. 481-487.

Cosimo Toma, Domenico Gadaleta, Alessandra Roncaglioni, Andrey Toropov, Alla Toropova, Marco Marzo, Emilio Benfenati,
QSAR development for plasma protein binding: influence of the ionization state.
Pharmaceutical Research, (2019) 36: 28.

Rosa Becerra, Robin Walsh,
Thermochemistry of Germanium and Organogermanium compounds.
Physical Chemistry Chemical Physics, 2019,21, 988-1008.

Kazeem O. Sulaiman, Temitope U. Kolapo, Abdulmujeeb T. Onawole, Md. Ataul Islam, Rukayat O. Adegoke & Suaibu O. Badmus,
Molecular dynamics and combined docking studies for the identification of Zaire Ebola Virus inhibitors.
Journal of Biomolecular Structure and Dynamics,37:12, 2019, 3029-3040.

Vanja P. Ničković, Zorica N. Vujnović-Živković, Rada Trajković, Dane Krtinić, Lidija Ristić, Milan Radović, Zorica Ćirić, Dušan Sokolović & Aleksandar M. Veselinović,
In silico studies and the design of novel agents for the treatment of systemic tuberculosis,
Journal of Biomolecular Structure and Dynamics, 37:12, 2019, 3198-3205.

Michael Gonzalez Durruthy, Silvana Manske Nunes, Juliane Ventura Lima, Marcos A Gelesky, Humberto González-Díaz, José M. Monserrat, Riccardo Concu, and M. Natalia D.S. Cordeiro,
MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM NanoDescriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors.
J. Chem. Inf. Model., 2019 Jan 28; 59(1): 86-97.

Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati,
Semi-correlations as a tool to build up categorical (active/inactive) model of GABA_A receptor modulators activity.
Struct. Chem. (2019) 30 (3): 853–861.

Probir Kumar Ojha, Supratik Kar, Kunal Roy & Jerzy Leszczynski,
Toward comprehension of multiple human cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity (QICLUS) modeling,
Nanotoxicology, 2019; 13(1): 14-34.

A.P. Toropova, A.A. Toropov, E. Benfenati, D. Leszczynska, J. Leszczynski,
Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique.
Anti-Cancer Agents in Medicinal Chemistry, 19(2), 2019, 148 – 153.

Alla P. Toropova, Andrey A. Toropov,
Quasi-SMILES: Quantitative Structure - Activity Relationships to predict anti-cancer activity.
Molecular Diversity, (2019) 23: 403–412.

Alla P. Toropova, Andrey A. Toropov, Aleksandar M. Veselinović, Jovana B. Veselinović, Danuta Leszczynska, Jerzy Leszczynski,
Semi-correlations combined with the index of ideality of correlation: A tool to build up model of mutagenic potential.
Molecular and Cellular Biochemistry, 2019, Volume 452, Issue 1–2, pp. 133–140.

Kumar P., Kumar A., Sindhu J., Lal S.,
QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method.
Drug Res. (Stuttg), 2019 Mar; 69(3): 159-167.

Andrey A. Toropov, Alla P. Toropova,
Use of The Index of Ideality of Correlation to improve predictive potential for biochemical endpoints.
Toxicology Mechanisms and Methods, 2019, 29(1), 43–52.

Alla P. Toropova, Andrey A. Toropov.
The index of ideality of correlation: Improvement of models for toxicity to algae.
Natural Product Research, 33(15), 2019, 2200-2207.

Andrey A. Toropov, Alla P. Toropova, Giuseppa Raitano, Emilio Benfenati,
CORAL: building up QSAR models for the chromosome aberration test.
Saudi Journal of Biological Sciences, 26 (2019) 1101-1106. https://doi.org/10.1016/j.sjbs.2018.05.013

Huaiyu Wen,
Application of Monte Carlo calculation method based on special graph in medical imaging.
Cluster Computing, Publisced online: 10 March 2018; DOI:10.1007/s10586-018-2332-7

Sonam Bhargava, Tarun Patel, Ruchi Gaikwad, Umesh Kumar Patil & Shovanlal Gayen,
Identification of structural requirements and prediction of inhibitory activity of natural flavonoids against Zika virus through molecular docking and Monte Carlo based QSAR Simulation.
Natural Product Research 33:6, 2019, 851-857, DOI: 10.1080/14786419.2017.1413574

• TO THE BEGINNING

2018

Cavalcanti É.B.V.S., Félix M.B., Scotti L., Scotti M.T.,
Virtual Screening of Natural Products to Select Compounds with Potential Anticancer Activity.
Anticancer Agents Med. Chem. 2019; 19(2): 154-171. doi: 10.2174/1871520618666181119110934

S. Chauhan & A. Kumar,
Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure,
SAR and QSAR in Environmental Research, 2018, 29:4, 277-294, DOI: 10.1080/1062936X.2018.1426626

Petito E.S., Foster D.J.R., Ward M.B., Sykes M.J.
Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties.
Curr. Top. Med. Chem. 2018; 18(26): 2230-2238.

Weihao Tang, Jingwen Chen, Zhongyu Wang, Hongbin Xie & Huixiao Hong,
Deep learning for predicting toxicity of chemicals: a mini review,
Journal of Environmental Science and Health, Part C, 36:4, 2018, 252-271. DOI: 10.1080/10590501.2018.1537563

Wilm A., Kühnl J., Kirchmair J.,
Computational approaches for skin sensitization prediction.
Crit Rev Toxicol. 2018 Oct;48(9):738-760.

Tripaldi P., Pérez-González A., Rojas C., Radax J., Ballabio D., Todeschini R.
Classification-based QSAR Models for the Prediction of the Bioactivity of ACE-inhibitor Peptides.
Protein Pept Lett. 2018; 25(11): 1015-1023.

Eleonore Fröhlich.
Comparison of conventional and advanced in vitro models in the toxicity testing of nanoparticles.
Artificial Cells, Nanomedicine, and Biotechnology, 2018; 46(sup2): 1091-1107.

Bai X., Yan L., Ji C., Zhang Q., Dong X., Chen A4, Zhao M.,
A combination of ternary classification models and reporter gene assays for the comprehensive thyroid hormone disruption profiles of 209 polychlorinated biphenyls.
Chemosphere, 2018; 210: 312-319.

V.V. Vazhev, B.G. Munarbaeva, E.M. Yergaliyeva, N.V. Vazheva, M.A. Gubenko,
Modeling of acute aqueous toxicity of organic compounds for Daphnia magna.
Chemistry Series. No 2(90)/2018 81. UDC 541.6 DOI: 10.31489/2018Ch2/81-85

A. Golbamaki, N. Golbamaki, N. Sizochenko, B. Rasulev, J. Leszczynski & E. Benfenati.
Genotoxicity induced by metal oxide nanoparticles: a weight of evidence study and effect of particle surface and electronic properties.
Nanotoxicology, 12:10, 2018, 1113-1129.

Utembe W., Wepener V., Yu I.J., Gulumian M.,
An assessment of applicability of existing approaches to predicting the bioaccumulation of conventional substances in nanomaterials.
Environ. Toxicol. Chem. 2018, 37: 2972-2988.

Alla Toropova; Andrey Toropov; Emilio Benfenati,
Idealized correlations: prediction of solubility of fullerene in organic solvents ,
Published: 10 December 2018 by MDPI AG in MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition
session WCUCW-02: West Coast University Capstone Workshop, WCU, Miami, USA, 2018 (doi: 10.3390/mol2net-04-05898)

Jang-Sik Choi, My Kieu Ha, Tung Xuan Trinh, Tae Hyun Yoon & Hyung-Gi Byun,
Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources.
Scientific Reports, volume 8, Article number: 6110 (2018).

Wang, T.; Tang, L.; Luan, F.; Cordeiro, M.N.D.S.
Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors.
Int. J. Mol. Sci. 2018, 19, 3423.

Bellampalli S.S., Ji Y., Moutal A., Cai S., Wijeratne E.M.K., Gandini M.A., Yu J., Chefdeville A., Dorame A., Chew L.A., Madura C.L., Luo S., Molnar G., Khanna M., Streicher J.M., Zamponi G.W., Gunatilaka A.A.L., Khanna R.,
Betulinic acid, derived from the desert lavender Hyptis emoryi, attenuates paclitaxel-, HIV-, and nerve injury–associated peripheral sensory neuropathy via block of N- and T-type calcium channels.
Pain. 2018 Aug 28. doi: 10.1097/j.pain.0000000000001385

A.K. Halder,
Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification.
SAR and QSAR in Environmental Research, (2018) 29:11, 911-933.

Andrey A. Toropov and Alla P. Toropova,
Predicting Cytotoxicity of 2-Phenylindole Derivatives Against Breast Cancer Cells Using Index of Ideality of Correlation.
Anticancer Research, 38: 6189-6194 (2018).

S. Ahmadi & A. Akbari,
Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method,
SAR and QSAR in Environmental Research,(2018) 29:11, 895-909.

Floresta, G.; Amata, E.; Barbaraci, C.; Gentile, D.; Turnaturi, R.; Marrazzo, A.; Rescifina, A.,
A Structure- and Ligand-Based Virtual Screening of a Database of “Small” Marine Natural Products for the Identification of “Blue” Sigma-2 Receptor Ligands.
Mar. Drugs 2018, 16 (10), 384.

T. Ferrari, A. Lombardo, E. Benfenati,
QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study.
Science of The Total Environment, 637–638, 2018, 1158-1165.

Sunil Kr. Jha, T.H. Yoon, Zhaoqing Pan,
Multivariate statistical analysis for selecting optimal descriptors in the toxicity modeling of nanomaterials,
Computers in Biology and Medicine, 99, 2018, 161-172.

K. Venko, V. Drgan, M. Novič,
Classification models for identifying substances exhibiting acute contact toxicity in honeybees (Apis mellifera).
SAR and QSAR in Environmental Research 2018; 29(9):743-754.

Alla P. Toropova, Andrey A. Toropov,
Use of the Index of Ideality of Correlation to improve models of Eco-toxicity.
Environmental Science and Pollution Research, (2018) 25: 31771-31775.

Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski,
The Index of Ideality of Correlation: Hierarchy of Monte Carlo models for Glass Transition Temperatures of Polymers.
Journal of Polymer Research,(2018) 25: 221-227.

Pathan Mohsin Khan, Bakhtiyor Rasulev, and Kunal Roy,
QSPR Modeling of the Refractive Index for Diverse Polymers Using 2D Descriptors.
ACS Omega 2018, 3(10), 13374-13386.

Morales J.F., Alberca L.N., Chuguransky S., Di Ianni M.E., Talevi A., Ruiz M.E.,
Molecular Topology and other Promiscuity Determinants as Predictors of Therapeutic Class- A Theoretical Framework to guide Drug Repositioning?
Curr. Top. Med. Chem. 18(13), 2018, 1110 - 1122.

L.Grajciar, C.J. Heard, A.A. Bondarenko, M.V. Polynski, J. Meeprasert, E.A. Pidko, P. Nachtigall,
Towards operando computational modeling in heterogeneous catalysis.
Chem. Soc. Rev., 2018,47, 8307-8348.

R.D. Handy, J. Ahtiainen, J. M. Navas, G. Goss, E. A. J. Bleekere and F. von der Kammer,
Proposal for a tiered dietary bioaccumulation testing strategy for engineered nanomaterials using fish.
Environ. Sci.: Nano, 2018,5, 2030-2046.

Laroche C., Aggarwal M., Bender H., Benndorf P., Birk B., Crozier J., Dal Negro G., De Gaetano F., Desaintes C., Gardner I., Hubesch B., Irizar A., John D., Kumar V., Lostia A., Manou I., Monshouwer M., Müller B.P., Paini A., Reid K., Rowan T., Sachana M., Schutte K., Stirling C., Taalman R., van Aerts L., Weissenhorn R., Sauer U.G.,
Finding synergies for 3Rs – Toxicokinetics and read-across: Report from an EPAA partners' Forum.
Regul Toxicol Pharmacol. 2018; 99: 5-21.

J. D. Rasinger,F. Frenzel, A. Braeuning, A. Lampen,
Identification and evaluation of potentially mutagenic and carcinogenic food contaminants.
EFSA Journal 16(11), 2018. DOI: 10.2903/j.efsa.2018.e16085

Wang Z., Yang H., Wu Z., Wang T., Li W., Tang Y., Liu G.,
In silico prediction of chemical blood-brain barrier permeability with machine learning and re-sampling methods.
Chem.Med.Chem. 2018, 13(20): 2189-2201.

Md Lutful Islam and Gulabchand K. Gupta.
Application of Monte Carlo Algorithm to Explore Simplified Molecular-Input Line-Entry System based Molecular Descriptors of BACE1 inhibitors for Therapeutic Application in Alzheimer’s Disease.
International Journal of Computer Applications 182(11): 40-47, 2018.

Jose Cordero Cortes,
PTML Knowledge-Based System for Multi-Output Prediction of Anti-Melanoma Compounds.
Conference: MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition, 2018. DOI: 10.3390/mol2net-04-05471

Q. Cao, L. Liu, H. Yang, Y. Cai, W. Li, G. Liu, P. W. Lee and Y. Tang,
In silico estimation of chemical aquatic toxicity on crustacean using chemical category methods.
Environ. Sci.: Processes Impacts, 2018 Sep 19; 20(9):1234-1243.

Vineet Kumar, Nandita Dasgupta, Shivendu Ranjan,
Nanotoxicology: Toxicity Evaluation, Risk Assessment and Management.
Edition: 1st Publisher: CRC Press, Florida, USA (Taylor and Francis Group).Editor: Vineet Kumar, Nandita Dasgupta, Shivendu Ranjan
ISBN: 9781498799416. Published March 26, 2018.

Choudri B.S., Charabi Y., Ahmed M.,
Pesticides and Herbicides.
Water Environ Res. 2018; 90(10):1663-1678.

Bopp S.K., Barouki R., Brack W., Dalla Costa S., Dorne J.C.M., Drakvik P.E., Faust M., Karjalainen T.K., Kephalopoulos S., van Klaveren J., Kolossa-Gehring M., Kortenkamp A., Lebret E., Lettieri T., Nørager S., Rüegg J., Tarazona J.V., Trier X., van de Water B., van Gils J., Bergman Å.,
Current EU research activities on combined exposure to multiple chemicals.
Environment international, 2018, 120: 544-562.

Afantitis A., Melagraki G., Tsoumanis A., Valsami-Jones E., Lynch I.,
A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints.
Nanotoxicology, 2018, 5: 1-18. DOI: 10.1080/17435390.2018.1504998

W. Shoombuatong, N. Schaduangrat, C. Nantasenamat,
Towards understanding aromatase inhibitory activity via QSAR modeling.
EXCLI Journal 2018; 17: 688-708.

E. Benfenati, A. Golbamaki, G. Raitano, A. Roncaglioni, S. Manganelli, F. Lemke, U. Norinder, Elena Lo Piparo, M. Honma, A. Manganaro & G. Gini
A large comparison of integrated SAR/QSAR models of the Ames test for mutagenicity,
SAR and QSAR in Environmental Research, 2018, 29(8): 591-611.

V. Stoičkov, S. Šarić, M. Golubović, D. Zlatanović, D. Krtinić, L. Dinić, B. Mladenović, D. Sokolović, A.M. Veselinović,
Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach.
SAR and QSAR in Environmental Research, 29(7), 2018, 503-515.

Ashwani Kumar, Shilpi Chauhan,
Use of Simplified Molecular Input Line Entry System and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors.
Future medicinal chemistry 10(13) (2018) 1603-1622.

Sanija Begum, P. Ganga Raju Achary, Andrey A. Toropov, Alla P. Toropova,
Simplified molecular-input line-entry system based quantitative structure–activity relationship (QSAR) models for Serotonin 3 (5-HT₃) receptor.
Indian Journal Of Chemistry Section-B, 57B, 2018, 1322-1327.

P. Montreer, S. Janaqi, S. Cariou, M. Chaignaud, I. Betremieux, P. Ricoux, F. Picard, S. Sirol, B. Assumani,J.-L. Fanlo, (2018)
Reliability Improvement of Odour Detection Thresholds Bibliographic Data. pp. 562-573.
In book: J. Medina, M. Ojeda-Aciego, J.L. Verdegay, D.A. Pelta (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_48

Lidia Ceriani, Andrea Ciacci, Rossella Baldin, Simona Kovarich, Manuela Pavan, Elena Fioravanzo, Arianna Bassan.
Final report on the update and maintenance of OpenFoodTox: EFSA's Chemical Hazards Database: S-IN Soluzioni Informatiche.
EFSA Journal, APPROVED: 25 May 2018. pp.1-59. doi:10.2903/sp.efsa.2018.EN-1438

Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Sara Castiglioni,Renzo Bagnati, Alice Passoni, Ettore Zuccato, Roberto Fanelli.
Quasi-SMILES as a tool to predict removal rates of pharmaceuticals and dyes in sewage.
Process Safety and Environmental Protection, 118 (2018) 227-233.

Caterina Leone, Elia E. Bertuzzi, Alla P.Toropova, Andrey A. Toropov, Emilio Benfenati.
CORAL: predictive models for cytotoxicity of functionalized nanozeolites based on quasi-SMILES.
Chemosphere, 210 (2018) 52-56.

Bureau R.
Nontest Methods to Predict Acute Toxicity: State of the Art for Applications of In Silico Methods.
In book: Computational Toxicology, Nicolotti O. (eds). Methods in molecular biology, Humana Press, New York, NY. June 2018, vol. 1800: 519-534. DOI: 10.1007/978-1-4939-7899-1_24

Amin, S.A., Bhargava, S., Adhikari, N., Gayen, S., Jha, T.
Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques.
Journal of Biomolecular Structure and Dynamics, 36 (3), (2018) 590-608.

R. Gaikwad, S. Ghorai, Sk. A. Amin, N. Adhikari, T. Patel, K. Das, T. Jha, S. Gayen.
Monte Carlo based modelling approach for designing and predicting cytotoxicity of 2-phenylindole derivatives against breast cancer cell line MCF7.
Toxicology in Vitro 52 (2018) 23–32.

P. Mikulskis, A. Hook, A.A. Dundas, D. Irvine, O. Sanni, D. Anderson, R. Langer, M.R. Alexander, P. Williams, D.A. Winkler.
Prediction of Broad-Spectrum Pathogen Attachment to Coating Materials for Biomedical Devices.
ACS Applied Materials & Interfaces, 2018, 10 (1), pp. 139–149.

Chen, M.; Yang, F.; Kang, J.; Gan, H.; Yang, X.; Lai, X.; Gao, Y.
Identfication of Potent LXRß-Selective Agonists without LXRa Activation by In Silico Approaches.
Molecules 2018, 23(6):1349.

A.M. Veselinović, A.A. Toropov, A.P. Toropova, D. Stanković-Đorđević and J.B. Veselinović,
Design and development of novel antibiotics based on FtsZ inhibition - in silico studies.
New Journal of Chemistry, 2018, 42, 10976-10982.

Alberca LN, Sbaraglini ML, Morales JF, Dietrich R, Ruiz MD, Pino Martínez AM, Miranda CG, Fraccaroli L, Alba Soto CD, Carrillo C, Palestro PH and Talevi A.
Cascade Ligand- and Structure-Based Virtual Screening to Identify New Trypanocidal Compounds Inhibiting Putrescine Uptake.
Front. Cell. Infect. Microbiol. (2018) 8:173.

Golubović M., Lazarević M., Zlatanović D., Krtinić D., Stoičkov V., Mladenović B., Milić D.J., Sokolović D., Veselinović A.M.
The anesthetic action of some polyhalogenated ethers - Monte Carlo method based QSAR study.
Computational Biology and Chemistry, Volume 75, 2018, Pages 32-38.

Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Danuta Leszczynska, Jerzy Leszczynski,
Prediction of antimicrobial activity of large pool of peptides using quasi-SMILES.
BioSystems,169-170 (2018) 5-12.

Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati, Jean Lou Dorne,
SAR for gastro-intestinal absorption and blood-brain barrier permeation of pesticides.
Chemico-Biological Interactions, 290 (2018) 1–5.

Slavov, S.H., Stoyanova-Slavova, I., Mattes, W., Beger, R.D., Brüschweiler, B.J.;
Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation.
Arch. Toxicol., 2018 Jul;92(7):2369-2384.

A.A. Toropov, A.P. Toropova, E. Benfenati, L. Diomede, M. Salmona,
Use of Quasi-SMILES to model biological activity of "micelle-polymer" samples.
Structural Chemistry (2018) 29: 1213–1223.

J.J. Villaverde, B. Sevilla-Morán, C. López-Goti, J.L. Alonso-Prados, P. Sandín-España.
Considerations of nano-QSAR/QSPR models for nanopesticide risk assessment within the European legislative framework.
Science of the Total Environment 634 (2018) 1530–1539.

Ferrari T., Lombardo A., Benfenati E.
QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study.
Science of the Total Environment 637–638 (2018) 1158–1165.

Chapa-González, C.; Piñón-Urbina, A.L.; García-Casillas, P.E.
Synthesis of Controlled-Size Silica Nanoparticles from Sodium Metasilicate and the Effect of the Addition of PEG in the Size Distribution.
Materials 2018, 11, 510.

Alla P. Toropova, Andrey A. Toropov,
Guest Editorial Preface:
Special Issue on Applications of QSPR/QSAR in Toxicology, Ecology, and Drug Discovery: Problems and Solutions.
International Journal of Quantitative Structure-Property Relationships, Volume 3, Issue 2, 2018.

Amin, S. A., Adhikari, N., Gayen, S., & Jha, T. (2018).
First Report on the Validated Classification-Based Chemometric Modeling of Human Rhinovirus 3C Protease (HRV 3Cpro) Inhibitors.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), 3(2), 1-20.

R. Gozalbes, J. Vicente de Julián-Ortiz,
Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation.
International Journal of Quantitative Structure-Property Relationships, Volume 3, Issue 1, 2018, 1-24.

Parvin Kumar, Ashwani Kumar,
Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles.
Drug Res (Stuttg) 2018; 68(04): 189-195.

Andrey A. Toropov, Natalia Sizochenko, Alla A. Toropova, Jerzy Leszczynski,
Towards the Development Of General Nano-Quantitative Structure-Property Relationship (nano-QSPR) Models: Zeta Potentials of Metal Oxide Nanoparticles.
Nanomaterials, 2018, 8(4), 243.

Zhai X., Chen M., Lu W.,
Predicting the toxicities of metal oxide nanoparticles based on support vector regression with a residual bootstrapping method.
Toxicology mechanisms and methods, 2018, 12:1-10.

Yi Yu, Huiyong Sun, Tingjun Hou, Suidong Wang, Youyong Li,
Fullerene derivatives act as inhibitors of leukocyte common antigen based on molecular dynamics simulations.
RSC Adv., 2018, 8, 13997-14008.

Tung Xuan Trinh, Jang-Sik Choi, Hyunpyo Jeon, Hyung-Gi Byun, Tae-Hyun Yoon, and Jongwoon Kim,
Quasi-SMILES-Based Nano-Quantitative Structure–Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells.
Chemical Research in Toxicology 2018, 31 (3), 183-190.

Gupta, S., Basant, N.
Predictive modeling: Solubility of C60and C70fullerenes in diverse solvents.
Chemosphere, 201, 2018, 361 - 369.

Andrey A. Toropov, Alla P. Toropova,
Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes.
Chemical Physics Letters, 701 (2018) 137-146.

Carbó-Dorca R.
Toward an universal quantum QSPR operator.
Int. J. Quantum. Chem. 2018; e25602. https://doi.org/10.1002/qua.25602

M. Chen, F. Jabeen, B. Rasulev, M. Ossowski, P. Boudjouk,
A computational structure-property relationship study of glass transition temperatures for a diverse set of polymers.
Journal of Polymer Science Part B Polymer Physics, 2018, 56, 877–885.

S. Begum, P.G.R. Achary,
Optimal descriptor based QSPR models for catalytic activity of propylene polymerization.
International Journal of Quantitative Structure-Property Relationships (IJQSPR), Volume 3, Issue 2, 2018, pp 36-48.

Andrey A. Toropov, Alla P. Toropova, Alessandra Roncaglioni, and Emilio Benfenati,
Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results.
Chapter 27, In Book: Orazio Nicolotti (ed.), Computational Toxicology: Methods and Protocols, Methods in Molecular Biology, vol. 1800, https://doi.org/10.1007/978-1-4939-7899-1_27, © Springer Science+Business Media, LLC, part of Springer Nature 2018.

Alves, V.M., Capuzzi, S.J., Braga, R.C., Borba, J.V.B., Silva, A.C., Luechtefeld, T., Hartung, T., Andrade, C.H., , Muratov, E.N., Tropsha, A.,
A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment.
ACS Sustainable Chemistry and Engineering, 2018, 6(3), pp. 2845-2859.

Chauhan, S., Kumar, A.,
Consensus QSAR modelling of SIRT1 activators using simplex representation of molecular structure.
SAR and QSAR in Environmental Research, 29(4), 2018, 277-294.

Bitam, S., Hamadache, M., Hanini, S.,
Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure–activity relationship modelling.
SAR and QSAR in Environmental Research, Volume 29, Issue 3, 4 March 2018, Pages 213-230.

Truong, L., Ouedraogo, G., Pham, L.L., Clouzeau, J., Loisel-Joubert, S., Blanchet, D., Noçairi, H., Setzer, W., Judson, R., Grulke, C., Mansouri, K., Martin, M.,
Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.
Archives of Toxicology, 92(2), 2018, 587-600.

Zhuang, J., Xing, X., Wang, D., Du, Z., Wang, J., Dong, Y., Yu, W., Siyal, S.H.,
Toxicity assessment of the extractables from multi-layer coextrusion poly ethylene bags exposed to pH=5 solution containing 4% benzyl alcohol and 0.1 M sodium acetate.
Regulatory Toxicology and Pharmacology, Volume 94, April 2018, Pages 47-56.

Veselinović, J.B., Ðordević, V., Bogdanović, M., Morić, I., Veselinović, A.M.,
QSAR modeling of dihydrofolate reductase inhibitors as a therapeutic target for multiresistant bacteria.
Struct. Chem. (2018) 29: 541-551.

Qin, L.-T., Chen, Y.-H., Zhang, X., Mo, L.-Y., Zeng, H.-H., Liang, Y.-P.,
QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide.
Chemosphere, 198 (2018) 122 - 129.

Xiuyun Zhai, Mingtong Chen, Wencong Lu, Dongping Chang,
Predicting specific surface areas of layered double hydroxides based on support vector regression integrated with a residual bootstrapping method.
J. Math. Chem. 2018, Volume 56, Issue 6, pp 1744–1758.

H. Hong, J. Zhu, M. Chen, P.Gong, C. Zhang, W. Tong,
In book: Drug-Induced Liver Toxicity, Editors: Chen, M., Will, Y.,
Chapter:Quantitative Structure–Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans.
March 2018, Springer Science+Business Media, LLC, part of Springer Nature. eBook ISBN: 978-1-4939-7677-5; Series ISSN: 1557-2153 DOI:10.1007/978-1-4939-7677-5_5

Joris T.K. Quik, Martine Bakker, Dik van de Meent, Mikko Poikkimäki, Miikka Dal Maso, Willie Peijnenburg,
Directions of QPPR development to complement the predictive models used in risk assessment of nanomaterials,
NanoImpact, Volume 11, July 2018, Pages 58-66.

Luana de Morais e Silva, Mateus Feitosa Alves, Luciana Scotti, Wilton Silva Lopes, Marcus Tullius Scotti,
Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches.
Ecotoxicology and Environmental Safety 153 (2018) 151–159.

Apilak Worachartcheewan, Alla P. Toropova, Andrey A. Toropov, Suphakit Siriwong, Jatupat Prapojanasomboon, Virapong Prachayasittikul and Chanin Nanatasenamat,
Quantitative structure–activity relationship study of betulinic acid derivatives against HIV using SMILES-based descriptors.
Current Computer-Aided Drug Design, 2018; 14(2): 152-159.

F. Luan, L. Tang, L. Zhang, S. Zhang, M. C. Monteagudo, M.N. D.S. Cordeiro,
A further development of the QNAR model to predict the cellular uptake of nanoparticles by pancreatic cancer cells.
Food and Chemical Toxicology 112 (2018) 571-580.

G. Melagraki, A. Afantitis,
Computational toxicology: From cheminformatics to nanoinformatics.
Food and Chemical Toxicology, 112, 2018,476-477.

Venkatraman, V.; Alsberg, B.K.
Designing High-Refractive Index Polymers Using Materials Informatics.
Polymers 2018, 10, 103.

S. Piovesana, A.L. Capriotti, C. Cavaliere, G. La Barbera, C.M. Montone, R. Z. Chiozzi, A. Laganà.
Recent trends and analytical challenges in plant bioactive peptide separation, identification and validation.
Anal. Bioanal. Chem. 2018, Volume 410, Issue 15, pp 3425–3444.

Barigye S.J., Freitas M.P., Ausina P., Zancan P., Sola-Penna M., Castillo-Garit J.A.,
Discrete Fourier Transform based Multivariate Image Analysis: Application to Modeling Aromatase Inhibitory Activity.
ACS Comb. Sci. 2018, 20(2):75-81.

Mbuso Faya, Rahul S. Kalhapure, Hezekiel M. Kumalo, Ayman Y. Waddad, Calvin Omolo,Thirumala Govender,
Conjugates and nano-delivery of antimicrobial peptides for enhancing therapeutic activity.
Journal of Drug Delivery Science and Technology, 44, April 2018, 153-171.

Andrey A. Toropov, Ramon Carbó-Dorca, Alla P. Toropova,
Index of Ideality of Correlation: new possibilities to validate QSAR: a case study.
Structural Chemistry, (2018) 29: 33–38.

Probir Kumar Ojha and Kunal Roy,
PLS regression-based chemometric modeling of odorant properties of diverse chemical constituents of black tea and coffee.
RSC Adv., 2018, 8, 2293-2304.

Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati, Mario Salmona,
Mutagenicity, Anticancer activity, and Blood brain barrier: Similarity and dissimilarity of molecular alerts.
Toxicology Mechanisms and Methods, 28(5), 2018, 321-327.

Alla P. Toropova and Andrey A. Toropov,
CORAL: QSAR Models for Carcinogenicity of Organic Compounds for Male and Female Rats.
Computational Biology and Chemistry, Volume 72, February 2018, Pages 26-32.

Ying Wang, Fengchang Wu, Yuedan Liu, Yunsong Mu, John P. Giesy, Wei Meng, Qing Hu, Jing Liu, Zhi Dang,
Effect doses for protection of human health predicted from physicochemical properties of metals/metalloids.
Environmental Pollution, 232, 2018, 458-466.

M. Zdravković, A. Antović, J. B. Veselinović, D.Sokolović, A. M. Veselinović,
QSPR in forensic analysis - The prediction of retention time of pesticide residues based on the Monte Carlo method.
Talanta, 2018, 178, 656-662.

Andrey A. Toropov, Alla P. Toropova, Luigi Cappellini, Emilio Benfenati, Enrico Davoli.
QSPR analysis of threshold of odor for the large number of heterogenic chemicals.
Molecular Diversity, (2018) 22:397–403.

Andrey A. Toropov and Alla P. Toropova.
Improved Model for Biodegradability of Organic Compounds: The Correlation Contributions of Rings.
Chapter 8 (pp. 147-183), In Book: Bidoia E., Montagnolli R. (eds) Toxicity and Biodegradation Testing. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. 2018. (https://rd.springer.com/protocol/10.1007%2F978-1-4939-7425-2_8)

A. Gajewicz,
How to judge whether QSAR/read-across predictions can be trusted? Novel approach for establishing model’s applicability domain.
Environ. Sci.: Nano, 2018, 5, 408-421

A. Gajewicz, T. Puzyn, K. Odziomek, P. Urbaszek, A. Haase, C. Riebeling, A. Luch, M.A. Irfan, R. Landsiedel, M. van der Zande, H. Bouwmeester,
Decision tree models to classify nanomaterials according to the DF4nanoGrouping scheme.
Nanotoxicology, 2018 Feb;12(1):1-17.

Duchowicz, P.R., Bacelo, D.E., Fioressi, S.E., Palermo, V., Ibezim, N.E., Romanelli, G.P.
QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors.
Medicinal Chemistry Research, (2018) 27: 420–428.

Puzyn T., Jeliazkova N., Sarimveis H., Marchese Robinson R., Lobaskin V., Rallo R., Richarz A., Gajewicz A., Papadopulos M., Hastings J., Cronin M.T.D., Benfenati E., Fernandez A.,
Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology.
Food Chem Toxicol.,Volume 112, February 2018, Pages 478-494.

V. Stoičkov, D. Stojanović, I. Tasic, S. Šarić, D. Radenković, P. Babović, D. Sokolović, A. M. Veselinović,
QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method.
Struct. Chem. (2018) 29: 441-449.

A. P. Toropova, A. A. Toropov, S. Begum, P. G. R. Achary,
Blood brain barrier and Alzheimer’s disease: Similarity and dissimilarity of molecular alerts.
Current Neuropharmacology, 2018, 16, 769-785.

A. P. Toropova, A.A. Toropov,
CORAL: Monte Carlo method to predict endpoints for medical chemistry.
Mini-Reviews in Medicinal Chemistry, 18(5), 2018, 382 - 391.

Guangchao Chen, Willie J.G.M. Peijnenburg, Yinlong Xiao, Martina G. Vijver,
Developing species sensitivity distributions for metallic nanomaterials considering the characteristics of nanomaterials, experimental conditions, and different types of endpoints.
Food and Chemical Toxicology, Volume 112, February 2018, Pages 563-570.

V. Kovalishyn, N. Abramenko, I. Kopernyk, L. Charochkina, L. Metelytsia, I.V. Tetko, W. Peijnenburg, L. Kustov,
Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform,
Food and Chemical Toxicology, Volume 112, February 2018, Pages 507-517.

Alla P. Toropova, Andrey A. Toropov, Marco Marzo, Sylvia E. Escher, Jean Lou Dorne, Nikolaos Georgiadis, Emilio Benfenati.
The application of new HARD-descriptor available from the CORAL software to building up NOAEL models.
Food and Chemical Toxicology 112 (2018) 544-550.

• TO THE BEGINNING

2017

Maham M., Nasrollahzadeh M., Bagherzadeh M., Akbari R.,
Green Synthesis of Palladium/Titanium Dioxide Nanoparticles and their Application for the Reduction of Methyl Orange, Congo Red and Rhodamine B in Aqueous Medium.
Comb. Chem. High Throughput Screen. 2017; 20(9): 787-795. doi: 10.2174/1386207320666171023154523

A. V. Fedorov, I. V. Shamanaev,
Crystal Structure Representation for Neural Networks using Topological Approach.
Mol. Inf. 2017, 36, 1600162.

Ravi Kant Upadhyay,
Chronic Kidney Diseases and Nanoparticle Therapeutics.
J. Tissue Sci. Eng. 2017, Vol 8(3): 209.

K. Venko, V. Drgan,Š. Župerl, M. Vračko, M. Novič,
In silico evaluation of toxicity towards honey bees with QSAR models.
Toxicology Letters 2017, 280:S281.

Fjodorova N., Novic M., Gajewicz A., Rasulev B.,
The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method.
Nanotoxicology, 2017; 11(4):475-483.

M. González-Durruthy, L.C. Alberici, C. Curti, Z. Naal, D.T. Atique-Sawazaki, J. M. Vázquez-Naya, H. González-Díaz, and C.R. Munteanu.
An Experimental-Computational Study of Carbon Nanotubes Effects on Mitochondrial Respiration: In Silico nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants.
Journal of Chemical Information and Modeling 57(5), 2017, 1029-1044. DOI: 10.1021/acs.jcim.6b00458

Le, T.C., Epa, V., Tran, L., Winkler, D.,
Chapter 4: Computational Approaches, pp. 83-102,
In book: Adverse Effects of Engineered Nanomaterials: Exposure, Toxicology, and Impact on Human Health: Second Edition, Exposure, Toxicology, and Impact on Human Health. 2017,
Edited by:Bengt Fadeel, Antonio Pietroiusti and Anna A. Shvedova, Elsevier Inc., ISBN: 978-0-12-809199-9, https://doi.org/10.1016/B978-0-12-809199-9.00004-5

Alla P. Toropova, Andrey A. Toropov,
Editorial. Special issue: Impact of Drug Metabolism and its Relevance upon Drug Discovery,
Current Drug Metabolism, 18(12) (2017) 1070.

Ali Rahmouni, Moufida Touhami, Tahar Benaissa.
Fukui Indices as QSAR Model Descriptors: The Case of the Anti-HIV Activity of 1-2-[(Hydroxyethoxy) Methyl]-6-(Phenylthio) Thymine Derivatives.
International Journal of Chemoinformatics and Chemical Engineering (IJCCE) 6(2), 2017, 14.

Guzialowska-Tic, J.,
The use of QSAR methods for determination of n-octanol/water partition coefficient using the example of hydroxyester HE-1.
E3S Web of Conferences, 19, 2017, Article number 02034.

Supratik Kar and Jerzy Leszczynski,
Recent Advances of Computational Modeling for Predicting Drug Metabolism: A Perspective.
Current Drug Metabolism, 18(12) 2017, 1106-1122.

Mikolajczyk A., Sizochenko N., Mulkiewicz E., Malankowska A., Nischk M., Jurczak P., Hirano S., Nowaczyk G., Zaleska-Medynska A., Leszczynski J., Gajewicz A., Puzyn T.,
Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: A complementary experimental and computational approach.
Beilstein J. Nanotechnol. 2017; 8: 2171-2180.

Jamal Shamsara,
EzQSAR: An R Package for Developing QSAR Models Directly From Structures.
The Open Medicinal Chemistry Journal, 2017, 11, 212-221.

Agnieszka Gajewicz,
Development of valuable predictive read-across models based on “real-life” (sparse) nanotoxicity data.
Environmental science: Nano, 2017, 4, 1389-1403.

Mariya A. Toropova,
Drug Metabolism as an Object of Computational Analysis by the Monte Carlo Method.
Current Drug Metabolism, 18(12) 2017,1123-1131.

L. Simon, A. Imane, K. K. Srinivasan, L. Pathak, I. Daoud,
In Silico Drug-Designing Studies on Flavanoids as Anticolon Cancer Agents: Pharmacophore Mapping, Molecular Docking, and Monte Carlo Method-Based QSAR Modeling.
Interdiscip. Sci. Comput. Life Sci. (2017) 9:445–458.

Bhargava S., Adhikari N., Amin S.A., Das K., Gayen S., Jha T.,
Hydroxyethylamine derivatives as HIV-1 protease inhibitors: a predictive QSAR modelling study based on Monte Carlo optimization.
SAR QSAR Environ Res. 2017, 28(12): 973-990.

Golmohammadi H., Dashtbozorgi Z., Khooshechin S.,
Modeling and predicting the solute polarity parameter in reversed-phase liquid chromatography using quantitative structure–property relationship approaches.
J. Sep. Sci. 2017, 40(23): 4495-4502.

Charly Empereur-mot.
Development of statistical tools for the evaluation of virtual screening methods: Predictiveness curves & Screening Explorer.
Thesis for: PhD in Biochemistry & Molecular Biology (Specialization: Bioinformatics), Advisor: Matthieu Montes & Jean-François Zagury.
June 2017.

Ortiz E.V., Bennardi D.O., Bacelo D.E., Fioressi S.E., Duchowicz P.R.,
The conformation-independent QSPR approach for predicting the oxidation rate constant of water micropollutants.
Environ. Sci. Pollut. Res. Int., 2017, 24(35): 27366-27375.

Shin H.K., Kim K.Y., Park J.W., No K.T.,
Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model.
SAR QSAR Environ Res. 2017 Nov, 30:1-14.

Amata E., Marrazzo A., Dichiara M., Modica M.N., Salerno L., Prezzavento O., Nastasi G., Rescifina A., Romeo G., Pittalà V.,
Heme Oxygenase Database (HemeOxDB) and QSAR analysis of isoform 1 inhibitors.
Chem. Med. Chem. 2017 Nov 22; 12(22):1873-1881.

Andrey A. Toropov,
Meet Our Editorial Board Member.
Current Drug Discovery Technologies, 2017, Vol. 14, No. 4, 215

Zhengwei Zhou, Xinwen Tang, Wen Dai, Jingjie Shi, Haiqun Chen,
Nano-QSAR models for predicting cytotoxicity of metal oxide nanoparticles (MONPs) to E. coli.
Canadian Journal of Chemistry, 2017, 95(8): 863-866.

Xu-Cheng Fu, Jiang-Zhou Jin, Ju Wu, Jun-Cheng Jin, Cheng-Gen Xie,
A novel "turn-on" fluorescence sensor for high selectively detecting Al(III) in aqueous solution based on simple electrochemical synthesized carbon dots.
Anal. Methods, 2017, 9, 3941-3948.

Wang, Y., Yan, F., Jia, Q., Wang, Q.,
Assessment for multi-endpoint values of carbon nanotubes: Quantitative nanostructure-property relationship modeling with norm indexes.
Journal of Molecular Liquids, 248, 2017, 399 - 405.

Baghemiyani, T.A., Kalantari Fotooh, F.,
Interaction of Lead Metal with Single Walled AlN Nanotube: A Computational Study.
Journal of Inorganic and Organometallic Polymers and Materials, 27(5), 2017, 1274-1280.

Podrazka, M., Báczynska, E., Kundys, M., Jelen, P.S., Nery, E.W.,
Electronic tongue-A tool for all tastes?
Biosensors, 8(1), 2017, 3.

S. Lin, M. Mortimer, R. Chen, A. Kakinen, J.E. Riviere, T.P. Davis, F. Ding, P.-C. Ke.
NanoEHS beyond toxicity – focusing on biocorona.
Environ. Sci.: Nano, 2017,4, 1433-1454.

González-Durruthy, M.; Monserrat, J.M.; Rasulev, B.; Casañola-Martín, G.M.; Barreiro Sorrivas, J.M.; Paraíso-Medina, S.; Maojo, V.; González-Díaz, H.; Pazos, A.; Munteanu, C.R.
Carbon Nanotubes’ Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra.
Nanomaterials 2017, 7, 386.

Varsou, D.-D., Melagraki, G., Sarimveis, H., Afantitis, A.,
MouseTox: An online toxicity assessment tool for small molecules through Enalos Cloud platform.
Food and Chemical Toxicology, 110, 2017, 83 - 93.

A. Allali,F. Ferkous, K. Kraim,Y. Saihi, M. Brahimi, F. Zaiz, O. Attoui-Yahia,
A nonlinear QSAR Study Using Oscillating Search and SVM as an Efficient Algorithm to Model the Inhibition of Reverse Transcriptase by HEPT Derivatives.
Journal- Chemical Society of Pakistan 2017, 40(1):24.

Aranda, J.F., Bacelo, D.E., Leguizamón Aparicio, M.S., Ocsachoque, M.A., Castro, E.A., Duchowicz, P.R.
Predicting the bioconcentration factor through a conformation-independent QSPR study.
SAR and QSAR in Environmental Research, 28 (9), (2017) pp. 749-763.

E. Amata, A. Marrazzo, M. Dichiara, M. N. Modica, L. Salerno, O. Prezzavento, G. Nastasi, A. Rescifina, G. Romeo, V. Pittalà,
Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors.
Data in Brief 15 (2017) 281–299.

Scott-Fordsmand, J.J.; Peijnenburg, W.J.G.M.; Semenzin, E.; Nowack, B.; Hunt, N.; Hristozov, D.; Marcomini, A.; Irfan, M.; Jiménez, A.S.; Landsiedel, R.; Tran, L.; Oomen, A.G.; Bos, P.M.J.; Hund-Rinke, K.
Environmental Risk Assessment Strategy for Nanomaterials.
Int. J. Environ. Res. Public Health 2017, 14, 1251.

M.González-Durruthy, A. V. Werhli, V. Seus, K. S. Machado, A. Pazos, C.R. Munteanu, H. González-Díaz, J. M. Monserrat,
Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory.
Scientific Reports 7, Article number: 13271 (2017).

Qin, L.; Zhang, X.; Chen, Y.; Mo, L.; Zeng, H.; Liang, Y.
Predictive QSAR Models for the Toxicity of Disinfection Byproducts.
Molecules 2017, 22, 1671.

Zhao Q., Lu Y., Zhao Y., Li R., Luan F., Cordeiro M.N.
Rational Design of Multi-Target Estrogen Receptors ERa and ERß by QSAR Approaches.
Curr Drug Targets. 2017;18(5):576-591.

D. Ballabio, F. Biganzoli, R. Todeschini & V. Consonni,
Qualitative consensus of QSAR ready biodegradability predictions.
Toxicological and Environmental Chemistry, Vol. 99 , Iss. 7-8,2017.

D. W. Boukhvalov, T. H. Yoon.
Development of Theoretical Descriptors for Cytotoxicity Evaluation of Metallic Nanoparticles.
Chem. Res. Toxicol. 2017, 30, 1549-1555.

Adhikari N., Amin S.A., Saha A., Jha T.,
Combating breast cancer with non-steroidal aromatase inhibitors (NSAIs): Understanding the chemico-biological interactions through comparative SAR/QSAR study.
Eur. J. Med. Chem. 2017, 137:365-438.

Nolte T.M., Ragas A.M.,
A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps.
Environmental Science: Processes and Impacts, 2017 ;19(3):221-246.

R. Costa, L. Santos,
Delivery systems for cosmetics - From manufacturing to the skin of natural antioxidants.
Powder Technology, 322, 2017, 402-416.

Reza Aalizadeh, Peter C. von der Ohe and Nikolaos S. Thomaidis,
Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization–Support Vector Machine QSTR models.
Environ. Sci.: Processes Impacts, 2017, 19, 438-448.

Enrico Burello,
Review of (Q)SAR models for regulatory assessment of nanomaterials risks.
NanoImpact 8 (2017) 48–58.

R. Concu, H. González-Díaz, N.D.S. Cordeiro,
Machine Learning Approach to Predict Enzyme Subclasses.
Chapter 2 In book: Multi-Scale Approaches in Drug Discovery, pp.37-53, 2017.
DOI: 10.1016/B978-0-08-101129-4.00002-3

Chen, G.; Vijver, M.G.; Xiao, Y.; Peijnenburg, W.J.
A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials.
Materials 2017, 10, 1013.

Bueso-Bordils, J.I., Perez-Gracia, M.T., Suay-Garcia, B., Duart, M.J., Martin Algarra, R.V., Lahuerta Zamora, L., Anton-Fos, G.M., Aleman Lopez, P.A.,
Topological pattern for the search of new active drugs against methicillin resistant Staphylococcus aureus.
European Journal of Medicinal Chemistry, volume 138, 2017, pp. 807 - 815.

Andrew G. Mercader, Daniel E. Bacelo & Pablo R. Duchowicz,
Different Encoding Alternatives for the Prediction of Halogenated Polymers Glass Transition Temperature by Quantitative Structure-Property Relationships.
International Journal of Polymer Analysis and Characterization, 22 (7), 2017, 639-648.

S. Dasgupta, T. Auth, G. Gompper,
Nano- and microparticles at fluid and biological interfaces.
Journal of Physics: Condensed Matter,29 (2017) 373003 (41pp).

Long, J., Youli, Q., Yu, L.,
Effect analysis of quantum chemical descriptors and substituent characteristics on Henry's law constants of polybrominated diphenyl ethers at different temperatures.
Ecotoxicology and Environmental Safety, volume 145, 2017, pp. 176 - 183.

Barzegar, A., Jafari Mousavi, S., Hamidi, H., Sadeghi, M.,
2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors.
Physica E: Low-dimensional Systems and Nanostructures, volume 93, 2017, pp. 324 - 331.

Karel Nesmerák, Andrey A. Toropov, Alla P. Toropova, Tugba Ertan-Bolelli, Ilkay Yildiz.
QSAR of antimycobacterial activity of benzoxazoles by optimal SMILES-based descriptors.
Med Chem Res (2017) 26: 3203-3208.

Tsakovska, I., Pajeva, I., Al Sharif, M., Alov, P., Fioravanzo, E., Kovarich, S., Worth, A.P., Richarz, A.-N., Yang, C., Mostrag-Szlichtyng, A., Cronin, M.T.D.
Quantitative structure-skin permeability relationships.
Toxicology 387 (2017) 27–42.

Gupta, S., Basant, N.,
Modeling the aqueous phase reactivity of hydroxyl radical towards diverse organic micropollutants: An aid to water decontamination processes,
Chemosphere 2017 Oct; 185:1164-1172.

A. Rescifina, G. Floresta, A. Marrazzo, C. Parenti, O. Prezzavento, G. Nastasi, M. Dichiara, E. Amata,
Sigma-2 receptor ligands QSAR model dataset.
Data in Brief 13 (2017) 514–535.

G. Chen, W. Peijnenburg, Y. Xiao, M. G. Vijver,
Current Knowledge on the Use of Computational Toxicology in Hazard Assessment of Metallic Engineered Nanomaterials.
Int. J. Mol. Sci. 2017, 18, 1504.

Benfenati E., Como F., Manzo M., Gadaleta D., Toropov A. and Toropova A.,
Developing innovative in silico models with EFSA's OpenFoodTox database.
EFSA supporting publication 2017:EN-1206. 19 pp. doi:10.2903/sp.efsa.2017.EN-1206

Maja Ponikvar-Svet, Diana N. Zeiger, Joel F. Liebman,
Interplay of thermochemistry and Structural Chemistry, the journal (volume 27, 2016, issues 3–4) and the discipline.
Structural Chemistry, 2017, Volume 28, Issue 4, pp. 1265–1273.

Chedik, L.; Mias-Lucquin, D.; Bruyere, A.; Fardel, O.
In Silico Prediction for Intestinal Absorption and Brain Penetration of Chemical Pesticides in Humans.
Int. J. Environ. Res. Public Health 2017, 14, 708.

Birgit Viira, Alfonso T. García-Sosa, Uko Maran.
Chemical Structure and Correlation Analysis of HIV-1 NNRT and NRT Inhibitors and Database-Curated, Published Inhibition Constants with Chemical Structure in Diverse Datasets.
Journal of Molecular Graphics and Modelling, 2017 Sep; 76: 205-223.

L. Zhang, H. Ai, W. Chen, Z.Yin, H. Hu, J. Zhu, J. Zhao, Q. Zhao, H. Liu,
CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.
Scientific Reports 7, Article number: 2118 (2017). doi:10.1038/s41598-017-02365-0

W. Shoombuatong, P. Prathipati, W. Owasirikul, A. Worachartcheewan, S. Simeon, N. Anuwongcharoen, J. E.S. Wikberg and C. Nantasenamat.
Towards the Revival of Interpretable QSAR Models.
Part I. Chapter 1, pp. 3-57. In Book: Advances in QSAR modeling. Volume 24 of the series Challenges and Advances in Computational Chemistry and Physics. Edited: Roy, K. Springer International Publishing AG, 25 May 2017.

Farukh Jabeen, Min Chen, Bakhtiyor Rasulev, Martin Ossowski, Philip Boudjouk,
Refractive indices of diverse data set of polymers: A computational QSPR based study.
Computational Materials Science, Volume 137, September 2017 , Pages 215-224.

S. Dasgupta, T. Auth, G. Gompper,
Nano- and Microparticles at Fluid and Biological Interfaces.
J. Phys.: Condens. Matter 29 (2017) 373003 (41pp).

Kumar, A., Chauhan, S.,
QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method.
(2017) Drug Research, 67 (3), pp. 156-162.

A. Rescifina, G. Floresta, A. Marrazzo, C. Parenti, O. Prezzavento, G. Nastasi, M. Dichiara, E. Amata,
Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis.
European Journal of Pharmaceutical Sciences, 2017 Aug 30; 106: 94-101.

Alla P. Toropova, Andrey A. Toropov, Marten Beeg, Marco Gobbi, Mario Salmona,
Utilization of the Monte Carlo method to build up QSAR models for hemolysis and cytotoxicity of antimicrobial peptides.
Current Drug Discovery Technologies, 14(4), 2017, 229-243.

A. A. Toropov, A. P. Toropova, M. Marzo, J.L. Dorne, N. Georgiadis, E. Benfenati,
QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA´s OpenFoodTox database.
Environmental Toxicology and Pharmacology, 53 (2017) 158–163.

Andrey A. Toropov, Alla P. Toropova.
The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models?
Mutation Research - Genetic Toxicology and Environmental Mutagenesis, 819 (2017) 31-37.

Pablo R. Duchowicz, Silvina E. Fioressi, Eduardo Castro, Karolina Wróbel, Nnenna E. Ibezim, and Daniel E. Bacelo.
Conformation-Independent QSAR Study on Human Epidermal Growth Factor Receptor-2 (HER2) Inhibitors.
ChemistrySelect 2017, 2, 3725-3731.

A. Pérez-Garrido, F. Girón-Rodríguez, A. Bueno-Crespo, J. Soto, H. Pérez-Sánchez, A. Morales Helguera.
Fuzzy clustering as rational partition method for QSAR.
Chemometrics and Intelligent Laboratory Systems. Volume 166, 15 July 2017, Pages 1–6.

Manganelli S., Benfenati E.
Chapter 22: Nano-QSAR Model for Predicting Cell Viability of Human Embryonic Kidney Cells. Methods Mol Biol. 2017; 1601: 275-290. in Book :Cell Viability Assays. Methods and Protocols (Editors: Gilbert, Daniel F., Friedrich, Oliver)

Andrey A. Toropov, Alla P. Toropova, Marten Beeg, Marco Gobbi, Mario Salmona,
QSAR model for Blood-Brain Barrier Permeation.
Journal of Pharmacological and Toxicological Methods 88 (2017) 7-18.

Georgia Melagraki, Evangelos Ntougkos, Vagelis Rinotas, Christos Papaneophytou, Georgios Leonis, Thomas Mavromoustakos, George Kontopidis, Eleni Douni, Antreas Afantitis, George Kollias,
Cheminformatics-aided discovery of smallmolecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-kB Ligand(RANKL).
PLoS Comput. Biol. 13(4), 2017, e1005372.

Basant N., Gupta S.,
Multi-target QSTR modeling for simultaneous prediction of multiple toxicity endpoints of nano-metal oxides.
Nanotoxicology. 2017 Apr; 11(3): 339-350.

Alla P. Toropova, Andrey A. Toropov.
Hybrid Optimal Descriptors as a Tool to Predict Skin Sensitization in accordance to OECD principles.
Toxicology Letters, 275 (2017) 57 - 66.

K. Bouhedjar, S. Manganelli, G. Gini, A. A. Toropov, A. P. Toropova, S. Ali-Mokhnache, D. Messadi,
QSAR Modeling useful in Anti-Cancer Drug Discovery: Prediction of ^V600EBRAF-Dependent P-ERK using Monte Carlo Method.
(2017) J. Med. Chem. Toxicol. 2(1): 1 - 6.

Alla P. Toropova, Andrey A. Toropov, Emilio Benfenati, Robert Rallo, Danuta Leszczynska and Jerzy Leszczynski
Development of Monte Carlo Approaches in Support of Environmental Research.
Chapter 12 (pages 453-469), In Book: Advances in QSAR modeling. Volume 24 of the series Challenges and Advances in Computational Chemistry and Physics. Edited: Roy, K. Springer International Publishing AG, 25 May 2017.
DOI: 10.1007/978-3-319-56850-8_12

Aleksandar M. Veselinović, Dragan Velimorović, Biljana Kaličanin, Alla Toropova, Andrey Toropov, Jovana Veselinović,
Prediction of Gas Chromatographic Retention Indices Based on Monte Carlo Method.
Talanta 168 (2017) 257 - 262.

Kunal Roy, Pravin Ambure, Rahul B. Aher,
How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models?
Chemometrics and Intelligent Laboratory Systems 162 (2017) 44 - 54.

Ganesan A., Barakat K.
Applications of computer-aided approaches in the development of hepatitis C antiviral agents.
Expert Opin Drug Discov. 2017 Apr;12(4):407-425.

M. Gonzalez-Durruthy, M. Castro, S. Manske Nunes, J. Ventura-Lima, L. C. Alberici, Z. Naal, D. T. Atique-Sawazaki, C. Curti, C. Pires Ruas, M. A. Gelesky, K. Roy, H. Gonzalez-Diaz, J.M. Monserrat.
QSPR/QSAR-based Perturbation Theory approach and mechanistic electrochemical assays on carbon nanotubes with optimal properties against mitochondrial Fenton reaction experimentally induced by Fe²⁺-overload.
Carbon 115 (2017), 312-330.

Abdolmaleki A., Ghasemi F., Ghasemi J.B.
Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications.
Chem Biol Drug Des. 2017 Feb; 89(2): 257-268.

A. Kumar, S. Chauhan,
Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors.
SAR and QSAR in Environmental Research, 2017, 28(3):179-197.

D. Sokolović, J. Ranković, V. Stanković, R. Stefanović, S. Karaleić, B. Mekić, V. Milenković, J. Kocić, A.M. Veselinović.
QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method.
Med. Chem. Res. April 2017, Volume 26, Issue 4, pp. 796 - 804.

Floris M., Raitano G., Medda R., Benfenati E.
Fragment prioritization on a large mutagenicity dataset.
Mol. Inf. 2017, 36, 1600133.

Heidari, A., Fatemi, M.H.
A Theoretical Approach to Model and Predict the Adsorption Coefficients of Some Small Aromatic Molecules on Carbon Nanotube.
(2017) Journal of the Chinese Chemical Society, 64 (3), pp. 289 - 295.

Van Bossuyt M., Van Hoeck E., Raitano G., Manganelli S., Braeken E., Ates G., Vanhaecke T., Van Miert S., Benfenati E., Mertens B., Rogiers V.,
(Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances.
Food and Chemical Toxicology 102 (2017) 109 - 119.

In Book: Modelling the Toxicity of Nanoparticles, Volume 947, 2017 of the series Advances in Experimental Medicine and Biology.
Editors: Lang Tran, Miguel A. Bañares, Robert Rallo
Chapter: "Compilation of Data and Modelling of Nanoparticle Interactions and Toxicity in the NanoPUZZLES Project",
A.-N. Richarz, A. Avramopoulos, E. Benfenati, A. Gajewicz, N. Golbamaki Bakhtyari, G. Leonis, R.L. Marchese Robinson, M.G. Papadopoulos, M.T.D. Cronin, T. Puzyn, pp.303-324.
DOI: 10.1007/978-3-319-47754-1_9

In Book: Modelling the Toxicity of Nanoparticles, Volume 947, 2017 of the series Advances in Experimental Medicine and Biology.
Editors: Lang Tran, Miguel A. Bañares, Robert Rallo
Chapter: "An Integrated Data-Driven Strategy for Safe-by-Design Nanoparticles: The FP7 MODERN Project",
M. Brehm, A. Kafka, M. Bamler, R. Kühne, G. Schüürmann, L. Sikk, J. Burk, P. Burk, T. Tamm, K. Tämm, S. Pokhrel, L. Mädler, A. Kahru, V. Aruoja, M. Sihtmäe, J. Scott-Fordsmand, P. B. Sorensen, L. Escorihuela, C. P. Roca, A. Fernández, F. Giralt, R. Rallo, pp. 257-301.
DOI:10.1007/978-3-319-47754-1_9

Alla P. Toropova, Andrey A. Toropov, Danuta Leszczynska, Jerzy Leszczynski.
CORAL and Nano-QFAR: Quantitative feature-activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co₃O₄, and TiO₂).
Ecotoxicology and Environmental Safety, 2017; 139: 404-407.

Alla P. Toropova, Andrey A. Toropov.
The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability?
Science of the Total Environment 586 (2017) 466-472.

Ashwani Kumar and Shilpi Chauhan.
Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors.
Arch. Pharm. Chem. Life Sci. 2017, 350, e1600268

Alla P. Toropova, Andrey A. Toropov.
CORAL: Binary classifications (active/inactive) for drug-induced liver injury.
Toxicology Letters, 268, 15 February 2017, 51-57.

Alla P. Toropova, Andrey A. Toropov.
Nano-QSAR in cell biology: Model of cell viability as a mathematical function of available eclectic data. Journal of Theoretical Biology, 416 (2017) 113-118.

Halder A. K., Achintya S. and Jha T.
Predictive Quantitative Structure Toxicity Relationship Study on Avian Toxicity of Some Diverse Agrochemical Pesticides by Monte Carlo Method: QSTR on Pesticides,
International Journal of Quantitative Structure-Property Relationships (IJQSPR) 2017, 2(1), 19-34.

Veda Prachayasittikula, Apilak Worachartcheewana, Alla P. Toropova, Andrey A. Toropov, Virapong Prachayasittikul, Chanin Nantasenamat. Large-scale classification of P -glycoprotein inhibitors using SMILES-based descriptors.
SAR and QSAR in Environmental Research,28(1), 2017, 1-16.

Mariya A. Toropova, Ivan Raska Jr, Alla P. Toropova, Maria Raskova.
CORAL software: analysis of impacts of pharmaceutical agents upon metabolism via the optimal descriptors.
Current Drug Metabolism, Vol. 18, No. 6, pages 1-11, 2017.

Alla P. Toropova, Andrey A. Toropov, Aleksandar M. Veselinović, Jovana B. Veselinović, Danuta Leszczynska, Jerzy Leszczynski
Quasi-SMILES as a novel tool for prediction of nanomaterials' endpoints.
Chapter 8, In book: "Multi-Scale Approaches in Drug Discovery: From Synthetic Methodologies and Biological Assays to In Silico Experiments and Back ", 1st ed.; Speck-Planche, A., Ed. Elsevier: Oxford, UK, 2017; pp 191-221.
doi: http://dx.doi.org/10.1016/B978-0-08-101129-4.00008-4 (http://www.sciencedirect.com/science/book/9780081011294)

Andrey A. Toropov, Alla P. Toropova, Francesca Como, Emilio Benfenati;
Quantitative structure–activity relationship models for bee toxicity.
Toxicological & Environmental Chemistry, 99: 7-8, 2017, 1117-1128.

Alla P. Toropova, P. Ganga Raju Achary, Andrey A. Toropov
Quasi-SMILES for Nano-QSAR Prediction of Toxic Effect of Al₂O₃ Nanoparticles.
Chapter 59 (pages 1573-1584), In book: Pharmaceutical Sciences: Breakthroughs in Research and Practice (2 Volumes) 2017 |Pages: 1584.
DOI: 10.4018/978-1-5225-1762-7

Andrey A. Toropov, Alla P. Toropova, Emilio Benfenati, Orazio Nicolotti, Angelo Carotti, Karel Nesměrak, Aleksandar M. Veselinović, Jovana B. Veselinović, Pablo R. Duchowicz, Daniel Bacelo, Eduardo A. Castro, Bakhtiyor F. Rasulev, Danuta Leszczynska, Jerzy Leszczynski
QSPR/QSAR Analyses by Means of the CORAL Software: Results, Challenges, Perspectives.
Chapter 36 (pages 929-955), In book: Pharmaceutical Sciences: Breakthroughs in Research and Practice (2 Volumes) 2017 |Pages: 1584.
DOI: 10.4018/978-1-5225-1762-7

• TO THE BEGINNING

2016

A. Paternò, G. Bocci, L. Goracci, G. Musumarra, S. Scirè,
Modelling the aquatic toxicity of ionic liquids by means of VolSurf in silico descriptors.
SAR and QSAR in Environmental Research, 27(1) (2016) 1-15.

Severo Vazquez-Prieto, Esperanza Paniagua, Florencio M. Ubeira, Humberto González-Dìaz.
QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for Predicting ”In Silico“ New Optimal Peptide Sequences and/or Boundary Conditions for Vaccine Development.
International Journal of Peptide Research and Therapeutics 22(4):445-450, December 2016.

Öztürk H., Ozkirimli E., Özgür A.,
A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction.
BMC Bioinformatics. 2016;17:128. doi: 10.1186/s12859-016-0977-x.

P. Ganga Raju Achary, Sanija Begum, Alla P. Toropova, Andrey A. Toropov,
A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler - Natta catalysts for propylene polymerization.
Materials Discovery, 5, August 2016, 22-28.

Bernardo, G., Deb, N., King, S. M. and Bucknall, D. G.
Phase behavior of blends of PCBM with amorphous polymers with different aromaticity.
J. Polym. Sci. Part B: Polym. Phys., (2016),54: 994-1001.

E. Papa, J. P. Doucet, A. Doucet-Panaye,
Computational approaches for the prediction of the selective uptake of magnetofluorescent nanoparticles into human cells.
RSC Adv., 2016,6, 68806-68818.

Cassano A, Marchese Robinson RL, Palczewska A, Puzyn T, Gajewicz A, Tran L, Manganelli S, Cronin MT.
Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials.
Altern Lab Anim. 2016 Dec; 44 (6): 533-556.

Bragazzi N., Toropov A.A., Toropova A.P., Pechkova E., Nicolini C.
Quasi-QSPR to Predict Proteins Behavior Under Various Concentrations of Drug Using Nanoconductometric Assay.
NanoWorld J. 2016, 2(4):71-77. http://dx.doi.org/10.17756/nwj.2016-000

Xiangying Xu, Lei Li, Fangyou Yan, Qingzhu Jia, Qiang Wang, Peisheng Ma,
Predicting solubility of fullerene C₆₀ in diverse organic solvents using norm indexes.
Journal of Molecular Liquids, Volume 223, November 2016, Pages 603-610.

Alla P. Toropova, Andrey A. Toropov, Maria Raskova, Ivan Raska Jr,
Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method.
Environmental Toxicology and Pharmacology 48 (2016) 278-285.

Wang S., Zhai C., Zhang Y., Yu Y., Zhang Y., Ma L., Li S., Qiao Y.,
Cardamonin, a Novel Antagonist of hTRPA1 Cation Channel, Reveals Therapeutic Mechanism of Pathological Pain.
Molecules. 2016 Aug 29; 21(9). pii: E1145. doi: 10.3390/molecules21091145

G. F. Mangiatordi, D. Alberga, C. D. Altomare, A. Carotti, M. Catto, S. Cellamare, D. Gadaleta, G. Lattanzi, F. Leonetti, L. Pisani, A. Stefanachi, D. Trisciuzzi, O. Nicolotti,
Mind the Gap! A Journey towards Computational Toxicology.
Mol. Inf. 2016, 35(8-9), 294-308.

Tamm, K., Sikk, L., Burk, J., Rallo, R., Pokhrel, S., Madler, L., Scott-Fordsmand, J. J., Burk, P., Tamm, T.
Parametrization of nanoparticles: development of full-particle nanodescriptors,
Nanoscale, 2016, 8(36), 16243-16250. DOI: 10.1039/C6NR04376C

Karel Dieguez Santana, Hai Pham The, Pedro Julio Villegas Aguilar, Huong Le Thi Thu, Juan A Castillo-Garit, Gerardo Casañola-Martín.
Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database.
Chemosphere 165 (2016) 434-441.

Caracciolo G., Farokhzad O.C., Mahmoudi M.
Biological Identity of Nanoparticles In Vivo: Clinical Implications of the Protein Corona.
Trends in Biotechnology, 2016. pii: S0167-7799(16)30149-4. DOI: 10.1016/j.tibtech.2016.08.011

Hassan, M.Z., Osman, H., Ali, M.A., Ahsan, M.J.
Therapeutic potential of coumarins as antiviral agents.
European Journal of Medicinal Chemistry, 123 (2016) 236-255.

D. Sokolović,D. Aleksić, V. Milenković, S. Karaleić, D. Mitić, J. Kocić, B. Mekić, J. B. Veselinović, A. M. Veselinović,
QSAR modeling of bis-quinolinium and bis-isoquinolinium compounds as acetylcholine esterase inhibitors based on the Monte Carlo method‒the implication for Myasthenia gravis treatment.
Med. Chem. Res. December 2016, Volume 25, Issue 12, pp 2989-2998.

K. Jagiello, M. Grzonkowska, M. Swirog, L. Ahmed, B. Rasulev, A. Avramopoulos, M. G.Papadopoulos, J. Leszczynski, T. Puzyn.
Advantages and limitations of classic and 3D QSAR approaches in nano-QSAR studies based on biological activity of fullerene derivatives.
J. Nanopart. Res. (2016) 18: 256.

J. F. Aranda, J. C. Garro Martinez, E. A. Castro, P. R. Duchowicz,
Conformation-Independent QSPR Approach for the Soil Sorption Coefficient of Heterogeneous Compounds.
Int. J. Mol. Sci. 2016, 17, 1247. doi:10.3390/ijms17081247

E. Aranzamendi, S. Arrasate, N. Sotomayor, H. Gonzalez-Dìaz, E. Lete,
Chiral Brønsted Acid-Catalyzed Enantioselective α-Amidoalkylation Reactions: A Joint Experimental and Predictive Study.
ChemistryOpen 2016, 5, 540.

A.A. Toropov, A. P. Toropova, L. Cappellini, E. Benfenati, E. Davoli.
Odor Threshold prediction by means of the Monte Carlo method.
Ecotoxicology and Environmental Safety 133 (2016) 390-394.

Yong Pan, Ting Li, Jie Cheng, Donatello Telesca, Jeffrey I. Zink, Juncheng Jiang,
Nano-QSAR modeling for predicting the cytotoxicity of metal oxide nanoparticles using novel descriptors.
RSC Adv., 2016,6, 25766-25775.

Andrey A. Toropov, P. Ganga Raju Achary, Alla P. Toropova.
Quasi-SMILES and nano-QFPR: The predictive model for zeta potentials of metal oxide nanoparticles.
Chemical Physics Letters, 660 (2016) 107-110.

Hristozov D., Gottardo S., Semenzin E., Oomen A., Bos P., Peijnenburg W., van Tongeren M., Nowack B., Hunt N., Brunelli A., Scott-Fordsmand J.J., Tran L., Marcomini A.
Frameworks and tools for risk assessment of manufactured nanomaterials.
Environ. Int. 2016 Oct; 95: 36-53. doi: 10.1016/j.envint.2016.07.016

Adriana Monica Radu, Ana Maria Josceanu, Daniel Dinculescu, Vasile Lavric.
Enhanced partition model of 4-nitrophenol in water-octanol system. Effects of association/dissociation processes.
Fluid Phase Equilibria, 427, 15 November 2016, 575-582.

S. Manganelli, E.Benfenati, A.Manganaro, S.Kulkarni, T. S. Barton-Maclaren and M. Honma,
New quantitative structure-activity relationship models improve predictability of Ames mutagenicity for aromatic azo compounds.
Toxicol. Sci. (2016) 153 (2): 316-326.

Rosa S. Kim , Nicolas Goossens , Yujin Hoshida.
Use of big data in drug development for precision medicine.
Expert Review of Precision Medicine and Drug Development, 1(3), 2016, 245-253.

M. A. Toropova, I. Raska Jr, A.A. Toropov, M. Raskova,
The utilization of the Monte Carlo technique for rational drug discovery.
Combinatorial Chemistry & High Throughput Screening. 2016, 19 (8), 676-687.

Speck-Planche, A., Kleandrova, V.V., Ruso, J.M., Cordeiro, M.N.D.S.,
First Multitarget Chemo-Bioinformatic Model to Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens.
Journal of Chemical Information and Modeling, 56 (3),2016, pp. 588-598.

Toropov A.A., Toropova A.P., Nesměrak K., Veselinović A.M., Veselinović J.B., Leszczynska D., Leszczynski J.
Development of the latest tools for building up "nano-QSAR": Quantitative features-property/activity relationships (QFPRs/QFARs).
 Chapter 12, In Book: Practical Aspects of Computational Chemistry IV. J. Leszczynski, M.K. Shukla (Eds.). Springer 2016. ISBN 78-1-4899-7699-4, p. 353-396.
DOI: 10.1007/978-1-4899-7699-4_12 http://rd.springer.com/chapter/10.1007%2F978-1-4899-7699-4_12

Drgan V., Żuperl Š., Vračko M., Como F., Novič M.,
Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas) using counter-propagation artificial neural networks and genetic algorithm.
SAR QSAR Environ. Res. 2016 Jun 20: 1-19. http://dx.doi.org/10.1080/1062936X.2016.1196388

Jaroslaw Polanski, Johann Gasteiger.
Computer Representation of Chemical Compounds.
Chapter In book (Eds: J. Leszczynski): Handbook of Computational Chemistry, pp 1-43. Publisher: Springer Science + Business Media Dordrecht.
DOI: 10.1007/978-94-007-6169-8_50-1

Georgios Leonis, Georgia Melagraki, Antreas Afantitis.
Open-Source Chemoinformatics Software.
Chapter In book (Eds: J. Leszczynski): Handbook of Computational Chemistry, pp 1-30. Publisher: Springer Science + Business Media Dordrecht.
DOI: 10.1007/978-94-007-6169-8_57-1

Iseult Lynch and Robert Gregory Lee.
In Support of the Inclusion of Data on Nanomaterials Transformations and Environmental Interactions into Existing Regulatory Frameworks.
Chapter In Book (Eds: F. Murphy, E. M. McAlea, M. Mullins): Managing Risk in Nanotechnology, pp.145-169. January 2016.
DOI: 10.1007/978-3-319-32392-3_9

Azadi Golbamaki , Emilio Benfenati.
In Silico Methods for Carcinogenicity Assessment.
Chapter in Book: (Eds: Benfenati E.)In Silico Methods for Predicting Drug Toxicity, Volume 1425 of the series Methods in Molecular Biology, Springer, New York. 17 June 2016, pp 107-119.
DOI:10.1007/978-1-4939-3609-0_6

Fabiola Pizzo , Emilio Benfenati.
In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs.
Chapter in Book: (Eds: Benfenati E.) In Silico Methods for Predicting Drug Toxicity, Volume 1425 of the series Methods in Molecular Biology, Springer, New York.17 June 2016, pp 163-176.
DOI:10.1007/978-1-4939-3609-0_9

D. Sokolović, V. Stanković, D. Toskić, L. Lilić, G. Ranković, J. Ranković, G. Nedin-Ranković, A. M. Veselinović.
Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis.
Structural Chemistry, October 2016, Volume 27, Issue 5, pp. 1511-1519.

Ramon Carbó-Dorca.
A study on Goldbach conjecture.
Journal of Mathematical Chemistry, (2016) 54: 1798.

C. Blazquez-Barbadillo, E. Aranzamendi, E. Coya, E. Lete, N. Sotomayor and H. Gonzalez-Diaz.
Perturbation Theory Model of Reactivity and Enantioselectivity of Palladium-catalyzed Heck-Heck cascade reactions.
RSC Advances 6(45) (2016) 38602-38610.

David E. Jones, Hamidreza Ghandehari, Julio C. Facelli.
A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles.
Computer Methods and Programs in Biomedicine 132 (2016) 93-103.

Z. Hu, J. Wahl, M. Hamburger, A. Vedani.
Molecular mechanisms of endocrine and metabolic disruption: An in silico study on antitrypanosomal natural products and some derivatives.
Toxicology Letters 252 (2016) 29-41.

Natalia Sizochenko and Jerzy Leszczynski.
Review of Current and Emerging Approaches for Quantitative Nanostructure-Activity Relationship Modeling: The Case of Inorganic Nanoparticles.
Journal of Nanotoxicology and Nanomedicine (JNN) 1(1), 2016, 1-16. DOI: 10.4018/JNN.2016010101

Stanislaw Jastrzębski, Damian Leśniak, Wojciech Marian Czarnecki.
Learning to SMILE(S).
Submitted on 19 Feb 2016 . Computer Science > Computation and Language. http://arxiv.org/abs/1602.06289v1

Saw Simeon, Ola Spjuth, Maris Lapins, Sunanta Nabu, Nuttapat Anuwongcharoen, Virapong Prachayasittikul, Jarl E.S. Wikberg, Chanin Nantasenamat.
Origin of aromatase inhibitory activity via proteochemometric modeling.
PeerJ (2016) 4, e1979. DOI 10.7717/peerj.1979

Loreto M. Valenzuela, Doyle D. Knight, Joachim Kohn,
Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets.
International Journal of Biomaterials, 2016(3):1-10. DOI:10.1155/2016/6273414

A. P. Toropova, A. A. Toropov, A. M. Veselinović, J. B. Veselinović, D. Leszczynska, J. Leszczynski,
Monte Carlo based QSAR models for toxicity of organic chemicals to Daphnia magna.
Environmental Toxicology and Chemistry, Vol. 35, No. 11, pp. 2691-2697, 2016. DOI: 10.1002/etc.3466

Afsane Heidari,Mohammad H. Fatemi,
Hybrid Docking-Nano-QSPR: An Alternative Approach for Prediction of Chemicals Adsorption on Nanoparticles.
Nano brief reports and reviews. Volume 11, Issue 07, July 2016, pp. 1650078. DOI: 10.1142/S1793292016500788

Md Ataul Islam, Tahir S. Pillay,
Simplified molecular input line entry system-based descriptors in QSAR modeling for HIV-protease inhibitors,
Chemometrics and Intelligent Laboratory Systems, Volume 153, 15 April 2016, Pages 67-74.

M. Gobbi, M. Beeg, M. A. Toropova, A. A. Toropov, M. Salmona,
Monte Carlo method for Predicting of Cardiac Toxicity: hERG blocker compounds.
Toxicology Letters, 250 (2016) 42-46.

Alla P. Toropova, Andrey A. Toropov, Serena Manganelli, Caterina Leone, Diego Baderna, Emilio Benfenati, Roberto Fanelli,
Quasi-SMILES as a tool to utilize eclectic data for predicting the behavior of nanomaterials.
NANOIMPACT,1 (2016) 60-64. DOI:10.1016/j.impact.2016.04.003

Andrew G. Mercader, Pablo R. Duchowicz.
Encoding alternatives for the prediction of polyacrylates glass transition temperature by quantitative structure-property relationships.
Materials Chemistry and Physics 172 (2016) 158-164.

Jovana B. Veselinović, Aleksandar M. Veselinović, Alla P. Toropova, Andrey A. Toropov,
The Monte Carlo technique as a tool to predict LOAEL.
European Journal of Medicinal Chemistry, 116 (2016) 71-75.

A. P. Toropova and A. A. Toropov,
Assessment of nano-QSPR models of organic contaminant absorption by carbon nanotubes for ecological impact studies.
Materials Discovery, 4 (2016) 22-28.

Andrey A. Toropov, Alla P. Toropova, Sanija Begum, P. Ganga Raju Achary,
Towards predicting the solubility of CO₂ and N₂ in different polymers using a Quasi-SMILES based QSPR approach.
SAR and QSAR in Environmental Research, 27(4) (2016) 293-301.

Alla P. Toropova, Andrey A. Toropov.
Evolution of Optimal Descriptors: Solved, Unsolved, and Unsoluble Tasks.
International Journal of Quantitative Structure-Property Relationships, 1 (2), 2016, 52-71.

Denis Fourches, Dongqiuye Pu, Liwen Li, Hongyu Zhou, Qingxin Mu, Gaoxing Su, Bing Yan, Alexander Tropsha.
Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles.
Nanotoxicology, Volume 10, Issue 3, 2016, pages 374-383. DOI: 10.3109/17435390.2015.1073397

Andrey A. Toropov, Alla. P. Toropova, Emilio Benfenati, Roberto Fanelli.
QSAR as a random event: selecting of the molecular structure for potential anti-tuberculosis agents.
Anti-Infective Agents, 2016, 14(1): 3-10.

Alla P. Toropova, Andrey A. Toropov.
QSPR model for dispersibility of graphene in various solvents.
Letters in Drug Design & Discovery, Vol. 13, No. 1, 2016, 514-520.

Alla P. Toropova, P.Ganga Raju Achary, Andrey A. Toropov.
Quasi-SMILES for Nano-QSAR prediction of toxic effect of Al₂O₃ nanoparticles.
Journal of Nanotoxicology and Nanomedicine, 1(1), 2016, 17-28.

Veselinović A.M., Veselinović J.B., Nikolić G.M., Toropova A.P., Toropov A.A.,
QSPR models for estimating retention in HPLC with the p solute polarity parameter based on the Monte Carlo method.
Structural Chemistry, (2016) 27: 821-828.

Toropova, A.P., Toropov, A.A., Rallo, R., Leszczynska, D., and Leszczynski, J.,
Nano-QSAR: Genotoxicity of multi-walled carbon nanotubes.
International Journal of Environmental Research, 10(1): 59-64, Winter 2016.

Dave Winkler.
Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials.
Toxicology and Applied Pharmacology, 2016 May 15; 299: 96-100.

Manganelli, S., Leone, C., Toropov, A.A., Toropova, A.P., Benfenati, E.
QSAR model for cytotoxicity of silica nanoparticles on human embryonic kidney cells.
Materials Today: Proceedings, Volume 3, Issue 3, 2016, Pages 847-854.

S. Kar, A. Gajewicz, K. Roy, J. Leszczynski, T. Puzyn,
Extrapolating between toxicity endpoints of metal oxide nanoparticles: Predicting toxicity to Escherichia coli and human keratinocyte cell line (HaCaT) with Nano-QTTR.
Ecotoxicology and Environmental Safety 126 (2016) 238-244.

Alla P. Toropova, Terry W. Schultz, Andrey A. Toropov,
Building up a QSAR model for toxicity towards Tetrahymena Pyriformis by the Monte Carlo method: A case of benzene derivatives.
Environmental Toxicology and Pharmacology, 42 (2016) 135-145.

Karel Nesměrák, Andrey A. Toropov, Alla P. Toropova,
Model for electrochemical parameters for 4-(benzylsulfanyl)pyridines calculated from the molecular structure.
Journal of Electroanalytical Chemistry, 766 (2016) 24-29.

S. Manganelli, C. Leone, A.A. Toropov, A.P. Toropova, E. Benfenati,
QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO₂ nanoparticles.
Chemosphere,144 (2016) 995-1001.

Alla P. Toropova, Andrey A. Toropov, Aleksandar M. Veselinović, Jovana B. Veselinović, Emilio Benfenati, Danuta Leszczynska, Jerzy Leszczynski,
Nano-QSAR: Model of mutagenicity of fullerene as a mathematical function of different conditions.
Ecotoxicology and Environmental Safety, 124 (2016) 32-36.

Abdallah Hiba, Arnaudguilhem Carine, Abdul Rahim Haifa, Lobinski Ryszard, Jaber Farouk,
Monitoring of twenty-two sulfonamides in edible tissues: Investigation of new metabolites and their potential toxicity.
Food Chemistry 192, 1 February 2016, 212-227.

• TO THE BEGINNING

2015

Yadav M.R., Barmade M.A., Tamboli R.S., Murumkar P.R.,
Developing steroidal aromatase inhibitors-an effective armament to win the battle against breast cancer.
Eur. J. Med. Chem. 13 November 2015; 105: 1-38.

Guangchao Chen, Martina G. Vijver, Willie J.G.M. Peijnenburg.
Summary and Analysis of the Currently Existing Literature Data on Metal-based Nanoparticles Published for Selected Aquatic Organisms: Applicability for Toxicity Prediction by (Q)SARs.
ATLA 43, 221-240, 2015.

V. Kovalishyn, W. Peijnenburg, I. Kopernyk, N. Abramenko, L. Metelytsia,
QSPR modelling for predicting toxicity of nanomaterials.
Conference: The 7th International Conference on Nanomaterials - Research & Application At: Brno, Czech Republic, October 2015.

Hanieh Malekzadeh, Mohammad Hossein Fatemi, Setareh Gorji.
Novel application of the CORAL software to model Cellular Uptake of Magnetofluorescent Nanoparticles in Pancreatic Cancer Cells.
5th Iranian Biennial Chemometrics Seminar, 25-26 Nov 2015. https://www.ics.ir/Files/Content/media/12454_file.pdf

Seyedeh Mozhgan Behgozin, Mohammad Hosein Fatemi and Kobra Samghani,
In silico prediction of cutaneous penetration rate of some chemicals from their molecular structural descriptors.
5th Iranian Biennial Chemometrics Seminar, 25-26 Nov 2015. https://www.ics.ir/Files/Content/media/12454_file.pdf

Afsane Heidari, Mohammad H. Fatemi
Modeling and prediction of adsorption behavior of nanotubes.
5th Iranian Biennial Chemometrics Seminar, 25-26 Nov 2015. https://www.ics.ir/Files/Content/media/12454_file.pdf

Veselinović J.B., Kocić G.M., Pavic A., Nikodinovic-Runic J., Senerovic L., Nikolić G.M., Veselinović A.M.
Selected 4-phenyl hydroxycoumarins: In vitro cytotoxicity, teratogenic effect on zebrafish (Danio rerio) embryos and molecular docking study.
Chem. Biol. Interact. 2015 Apr 25; 231: 10-17.

Emilio Xavier Esposito, Anton J. Hopfinger, Chi-Yu Shao, Bo-Han Su, Sing-Zuo Chen, Yufeng Jane Tseng.
Exploring possiblemechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models.
Toxicology and Applied Pharmacology, 288 (2015) 52-62.

Sudiksha Aggrawal, Indu Chauhan, and Paritosh Mohanty,
Immobilization of Bi₂O₃ nanoparticles on the cellulose fibers of paper matrices and investigation of its antibacterial activity against E. coli in visible light.
Mater. Express, Vol. 5, No. 5, 2015, 429-436.

Toropov A.A.,
Alzheimer's disease: SMILES to preserve wisdom. December 22, 2015.
Available on the Atlas of Science website: http://atlasofscience.org/alzheimers-disease-smiles-to/

Michael Gonzalez-Durruthy, Jose Maria Monserrat, Luciane C. Alberici, Zeki Naal, Carlos Curti and Humberto Gonzalez-Diaz.
Mitoprotective activity of oxidized carbon nanotubes against mitochondrial swelling induced in multiple experimental conditions and predictions with new expected-value perturbation theory.
RSC Adv., 2015, 5, 103229-103245.

Pingaew R., Prachayasittikul V., Worachartcheewan A., Nantasenamat C., Prachayasittikul S., Ruchirawat S., Prachayasittikul V.
Novel 1,4-naphthoquinone-based sulfonamides: Synthesis, QSAR, anticancer and antimalarial studies.
European Journal of Medicinal Chemistry, 103 (2015) 446 - 459.

Jeganathan Manivannan, Thangarasu Silambarasan, Rajendran Kadarkarairaj, Boobalan Raja.
Systems pharmacology and molecular docking strategies prioritize natural molecules as cardioprotective agents.
RSC Adv. 2015, 5(94), 77042-77055.

Toropova, A.; Toropov, A.
CORAL: The dispersion of SWNTs in different organic solvents.
In Proceedings of the MOL2NET, 5-15 December 2015; Sciforum Electronic Conference Series, Vol. 1, 2015, c007;
doi:10.3390/MOL2NET-1-c007. http://sciforum.net/conference/MOL2NET-1/MOL2NET-c

Toropov A.A., Toropova A.P.
The CORAL software as spyglass to detect "coral reefs" in ocean of nanotechnologies. November 11, 2015.
Available on the Atlas of Science website: http://atlasofscience.org/the-coral-software-as-spyglass-to-detect-coral-reefs-in-ocean-of-nanotechnologies/

S. Gupta, N. Basant & K.P. Singh,
Predicting the hazardous dose of industrial chemicals in warm-blooded species using machine learning-based modelling approaches.
SAR and QSAR in environmental research, 2015 Jun; 26 (6):479-98.

Richard L. Marchese Robinson, Mark T. D. Cronin, Andrea-Nicole Richarz, Robert Rallo.
An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology.
Beilstein Journal of Nanotechnology 10/2015; 6:1978-1999.

Paula V. Messina, Jose Miguel Besada-Porto, Humberto Gonzalez-Diaz, and Juan M. Ruso,
Self-Assembled Binary Nanoscale Systems: Multi-Output Model with LFER-Covariance Perturbation Theory and Experimental-Computational Study of NaGDC-DDAB Micelles.
Langmuir, 2015, 31(44), 12009-12018. DOI: 10.1021/acs.langmuir.5b03074

Chun-I Wang and Chi-Chung Hua,
Solubility of C60 and PCBM in Organic Solvents.
The Journal of Physical Chemistry B. 2015, 119 (45), pp. 14496-14504.

Jiali Ying, Ting Zhang, and Meng Tang.
Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms.
Nanomaterials, 2015, 5, 1620-1637.

Xiaohui Jin, Sigrid Peldszus, Peter M. Huck.
Predicting the reaction rate constants of micropollutants with hydroxyl radicals in water using QSPR modeling.
Chemosphere, 138 (2015) 1-9.

Xiuchao Wu, Qingzhu Zhang, Hui Wang, Jingtian Hu.
Predicting carcinogenicity of organic compounds based on CPDB.
Chemosphere, 139, November 2015, 81-90.

Claudia Ileana Cappelli, Emilio Benfenati, Josep Cester.
Evaluation of QSAR models for predicting the partition coefficient (log P) of chemicals under the REACH regulation.
Environmental Research, 143 (2015) 26-32.

Karel Nesměrák, Andrey A. Toropov, Alla P. Toropova, Ilkay Yildiz, Ismail Yalcin, Marketa Brozikova, Vera Klimešová, Karel Waisser,
Prediction of Retention Characteristics of Heterocyclic Compounds.
Analytical and Bioanalytical Chemistry,(2015) 407: 9185-9189.

Papa E., Doucet J.P., Doucet-Panaye A.,
Linear and non-linear modelling of the cytotoxicity of TiO₂ and ZnO nanoparticles by empirical descriptors.
SAR QSAR Environ Res. (2015 Sep) 2: 1-19.

M. A. Toropova, A. M. Veselinović, J. B. Veselinović, D. B. Stojanović, A. A. Toropov.
QSAR modeling of the antimicrobial activity of peptides as a mathematical function of a sequence of amino acids.
Computational Biology and Chemistry, 59, Part A, December 2015, 126-130.

Shibi I.G., Aswathy L., Jisha R.S., Masand V.H., Divyachandran A., Gajbhiye J.M.,
Molecular docking and QSAR analyses for understanding the antimalarial activity of some 7-substituted-4-aminoquinoline derivatives.
European Journal of Pharmaceutical Sciences, (2015) 77: 9-23.

A. M. Veselinović, J. B. Veselinović, A. A. Toropov, A. P. Toropova, G. M. Nikolić,
In Silico Prediction of the β-Cyclodextrin Complexation Based on Monte Carlo Method.
International Journal of Pharmaceutics, 2015 Aug 28; 495(1): 404-409.

S.E. Fioressi, D.E. Bacelo, W.P. Cui, L.M. Saavedra, P.R. Duchowicz,
QSPR study on refractive indices of solvents commonly used in polymer chemistry using flexible molecular descriptors.
SAR and QSAR in Environmental Research, (2015 Jun) 26(6): 499-506.

Aleksandra Rybacka, Christina Ruden, Igor V. Tetko, Patrik L. Andersson,
Identifying potential endocrine disruptors among industrial chemicals and their metabolites - development and evaluation of in silico tools.
Chemosphere, (2015) 139: 372-378.

Ambure P., Aher R. B., Gajewicz A., Puzyn T., & Roy K. ,
"NanoBRIDGES"software: Open access tools to perform QSAR and nano-QSAR modeling.
Chemometrics and Intelligent Laboratory Systems, (15 October 2015) 147:1-13.

J. V. Zivković, N. V. Trutić, J. B. Veselinović, G. M. Nikolić, A. M. Veselinović,
Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3ß inhibitors.
Computers in Biology and Medicine, (2015) 64: 276-282.

Huicen Zhu, Weimin Guo, Zhemin Shen, Qingli Tang, Wenchao Ji, Lijuan Jia,
QSAR models for degradation of organic pollutants in ozonation process under acidic condition.
Chemosphere, (2015) 119: 65-71.

A. P. Toropova, A.A. Toropov, and E. Benfenati,
CORAL: Prediction of binding affinity and efficacy of thyroid hormone receptor ligands.
Journal of Medicinal Chemistry, (2015) 101: 452-461.

Apilak Worachartcheewan, Virapong Prachayasittikul, Alla P. Toropova, Andrey A. Toropov, Chanin Nantasenamat,
Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.
Molecular Diversity, 2015; 19: 955-964.

S. Begum, P. Ganga Raju Achary.
Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1).
SAR and QSAR in environmental research, (2015) 26(5): 343-361

Abdolmohammad Ghaedi.
Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors,
Journal of Molecular Liquids 208 (2015): 269-279

L. Quesada-Romero,K. Mena-Ulecia, M. Zuñiga, P. De-la-Torre, D. Rossi, W. Tiznado, S. Collina, J. Caballero.
Optimal graph-based and Simplified Molecular Input Line Entry System-based descriptors for quantitative structure-activity relationship analysis of arylalkylaminoalcohols, arylalkenylamines, and arylalkylamines as σ₁ receptor ligands.
J. Chemometrics, 2015, 29: 13-20.

J. B. Veselinović, G. M. Nikolić, N. V. Trutić, J. V. Zivković, A. M. Veselinović,
Monte Carlo QSAR models for predicting organophosphate inhibition of acetycholinesterase.
SAR and QSAR in environmental research, 2015; Jun 4: 1-12.

A. P. Toropova, A. A. Toropov, V. O. Kudyshkin, R. Rallo.
Prediction of the Q-e parameters from structures of transfer chain agents,
Journal of Polymer Research, 22 (2015): 128.

M. A. Toropova, A. A. Toropov, I. Raska Jr, M. Raskova.
Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method.
Computers in Biology and Medicine, 64 (1September 2015 ): 148-154.

A. P. Toropova, A. A. Toropov.
Quasi-SMILES and nano-QFAR: United model for mutagenicity of fullerene and MWCNT under different conditions.
Chemosphere, 139 (2015): 18-22.

H. Yilmaz, N. Novoselska, B. Rasulev, A.A. Toropov, Y. Guzel, V. Kuz'min, D. Leszczynska, J. Leszczynski.
Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor inhibitors: Performance of structure - activity relationship approaches.
Journal of food and drug analysis 23 (2015): 168-175.

Andrey A. Toropov, Robert Rallo, Alla P. Toropova.
Use of quasi-SMILES and Monte Carlo optimization to develop quantitative feature property/activity relationships (QFPR/QFAR) for nanomaterials.
Current Topics in Medicinal Chemistry, 2015, 15 ( 18 ): 1837-1844.

Ceyda Oksel, Cai Y. Ma, Jing J. Liu, Terry Wilkins, Xue Z. Wang.
(Q)SAR modelling of nanomaterial toxicity: A critical review.
Particuology, 21 (2015): 1-19.

A. Mikolajczyk, A. Gajewicz, B. Rasulev, N. Schaeublin, E. Maurer-Gardner, S. Hussain, J. Leszczynski, T. Puzyn.
Zeta Potential for Metal Oxide Nanoparticles: A Predictive Model Developed by a Nano-Quantitative Structure-Property Relationship Approach,
Chem. Mater. 2015, 27 (7): 2400-2407.

G.Melagraki and A. Afantitis.
A risk assessment tool for the virtual screening of metal oxide nanoparticles through Enalos In Silico Nano Platform,
Current Topics in Medicinal Chemistry, 2015, 15( 18 ): 1827-1836.

M.Salahinejad.
Nano-QSPR Modelling of Carbon-based Nanomaterials Properties,
Current Topics in Medicinal Chemistry, 2015, 15( 18 ): 1868-1886.

A. M. Veselinović, J. B. Veselinović, J. V. Zivković, G.M. Nikolić.
Application of SMILES notation based optimal descriptors in drug discovery and design,
Current Topics in Medicinal Chemistry, 2015, 15( 18 ): 1768-1779.

Xiaojia He, Winfred G. Aker, Ming-Ju Huang, John D. Watts, Huey-Min Hwang.
Metal Oxide Nanomaterials in Nanomedicine: Applications in Photodynamic Therapy and Potential Toxicity,
Current Topics in Medicinal Chemistry, 2015, 15( 18 ):1887-1900.

Liu, R., Rallo, R., Bilal, M., Cohen, Y.
Quantitative structure-activity relationships for cellular uptake of surface-modified nanoparticles.
Combinatorial Chemistry and High Throughput Screening, 18 (4),(2015) pp. 365-375.

H. Reis, B. Rasulev, M.G. Papadopoulos, J. Leszczynski.
Reliable but Timesaving: In Search of an Efficient Quantum-chemical Method for the Description of Functional Fullerenes,
Current Topics in Medicinal Chemistry, 2015, 15( 18 ): 1845-1858.

Andrey A. Toropov, Alla P. Toropova,
Editorial. Special issue: "From Chemoinformatics to Nanoinformatics: New tools for Drug Discovery and Nanoparticles Design in Medicinal Chemistry",
Current Topics in Medicinal Chemistry, 2015 May 6, 15( 18 ): 1767.

A. Speck-Planche, V. V. Kleandrova, F. Luan, M. N. D.S. Cordeiro.
Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model.
Nanomedicine, 2015, 10 (2): 193-204.

A. A. Toropov, A. P. Toropova, C. I. Cappelli, E. Benfenati.
CORAL: model for octanol/water partition coefficient.
Fluid Phase Equilibria, (2015) 397: 44-49.

A. A. Toropov, A. P. Toropova, F. Pizzo, A. Lombardo, D. Gadaleta, E. Benfenati.
CORAL: Model for No Observed Adverse Effect Level (NOAEL).
Molecular Diversity, (2015) 19(3): 563-575.

A. P. Toropova, A. A. Toropov, J. B. Veselinović, A. M. Veselinović,
QSAR as a random event: a case of NOAEL.
Environ. Sci. Poll. Res. (2015) 22(11): 8264-8271.

Maja Ponikvar-Svet; Diana N. Zeiger ; Joel F. Liebman,
Interplay of thermochemistry and structural chemistry, the journal (vol. 25, 2014, issues 1-2) and the discipline.
Structural Chemistry, 2015.

M. Cassotti, D. Ballabio, R. Todeschini, V. Consonni,
A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas).
SAR and QSAR in Environmental Research. (03/2015) 26(3): 217-243.

Jongwoon Kim, Sanghun Kim.
State of the art in the application of QSAR techniques for predicting mixture toxicity in environmental risk assessment.
SAR and QSAR in Environmental Research. (01/2015) 26(1): 41-59.

C. Oksel, C.Y. Ma, X.Z. Wang,
Current situation on the availability of nanostructure-biological activity data.
SAR and QSAR in Environmental Research, (01/2015) 26(2): 79-94.

Fatemi, M.H., Malekzadeh, H.
CORAL: Predictions of retention indices of volatiles in cooking rice using representation of the molecular structure obtained by combination of SMILES and graph approaches.
Journal of the Iranian Chemical Society, (2015) 12(3): 405-412.

A. P. Toropova, A. A. Toropov, J.B. Veselinović, A. M. Veselinović, E. Benfenati, D. Leszczynska, J. Leszczynski.
Application of the Monte Carlo method to prediction of dispersibility of graphene in various solvents.
Int. J. Environ. Res., 9(4):1211-1216, Autumn 2015.

A. P. Toropova and A. A. Toropov.
Mutagenicity: QSAR - quasi-QSAR - nano-QSAR.
Mini-Reviews in Medicinal Chemistry, (2015) 15(2): 608-621.

A. M. Veselinović, J. B. Veselinović, A. A. Toropov, A. P. Toropova, G. M. Nikolić.
QSAR Models for the Reactivation of Sarin Inhibited AChE by Quaternary Pyridinium Oximes Based on Monte Carlo Method.
Current computer-aided drug design, (2015) 10(3): 266-273.

A.P. Toropova , A. A. Toropov , E.Benfenati , D. Leszczynska , J. Leszczynski.
QSAR model as a random event: A case of rat toxicity.
Bioorganic & Medicinal Chemistry, (2015) 23(6): 1223-1230.

A. A. Toropov, A. P. Toropova, A. M. Veselinović, J. B. Veselinović, K. Nesměrak, I. Raska Jr, P. R. Duchowicz, E. A. Castro, V. O. Kudyshkin, D. Leszczynska, J. Leszczynski.
The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials : two examples of application.
Combinatorial Chemistry & High Throughput Screening. (2015) 18(4): 376-386.

A.A. Toropov, A. P. Toropova, E. Benfenati, O. Nicolotti, A. Carotti, K. Nesměrak, A. M. Veselinović, J. B. Veselinović, P. R. Duchowicz, D. Bacelo, E. A. Castro, B. F. Rasulev, D. Leszczynska, J. Leszczynski, QSPR/QSAR analyses by means of the CORAL software: results, challenges, perspectives.
Chapter 15, in Book: Roy, K. (2015). Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment (pp. 1-531). Hershey, PA: IGI Global. doi:10.4018/978-1-4666-8136-1
http://www.igi-global.com/book/quantitative-structure-activity-relationships-drug/120080

A. Gissi, A. Lombardo, A. Roncaglioni, D. Gadaleta, G. F. Mangiatordi, O. Nicolotti, E. Benfenati.
Evaluation and comparison of benchmark QSAR models to predict a relevant REACH endpoint: The bioconcentration factor (BCF).
Environmental Research 137(2015)398-409.

Chanchal Mondal, Amit Kumar Halder, Nilanjan Adhikari, Achintya Saha, Krishna Das Saha, Shovanlal Gayen, Tarun Jha.
Comparative validated molecular modeling of p53-HDM2 inhibitors as antiproliferative agents.
European Journal of Medicinal Chemistry 90 (2015) 860-875.

Toropova A.P., Toropov A.A., Benfenati E.
A quasi-QSPR modeling for the photocatalytic decolorisation rate constants and cellular viability (CV%) of nanoparticles by CORAL.
SAR and QSAR in Environmental Research. Jan 2015, 26(1); 29-40.

M. Sztanke , T. Tuzimski, M. Janicka , K. Sztanke.
Structure-retention behaviour of biologically active fused 1,2,4-triazinones - Correlation with in silico molecular properties.
European Journal of Pharmaceutical Sciences. (2015); 68: 114-126.

Eleni Vrontaki, Georgia Melagraki, Thomas Mavromoustakos, Antreas Afantitis.
Exploiting ChEMBL database to identify indole analogues as HCV replication inhibitors.
Methods. (2015); 71(1): 4-13.

A. A. Toropov, A.P. Toropova.
Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes.
Chemosphere (2015); 124: 40-46.

A. P. Toropova, A. A. Toropov, R. Rallo, D. Leszczynska, J. Leszczynski.
Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions.
Ecotoxicology and Environmental Safety. (2015); 112, 39–45.

J. B. Veselinović, A. A. Toropov, A. P. Toropova, G. M. Nikolić, A. M. Veselinović.
Monte Carlo Method-Based QSAR Modeling of Penicillins Binding to Human Serum Proteins.
Arch. Pharm. (2015); 348(1), 62-67.

Wendy A. Warr,
Many InChIs and quite some feat,
J. Comput. Aided Mol. Des. (2015) 29:681-694.

A. A. Toropov, J. B. Veselinović, A. M. Veselinović, F. N. Miljković, A. P. Toropova,
QSAR models for 1,2,4-benzotriazines as Src inhibitors based on Monte Carlo method.
Med. Chem. Res. (2015); 24 (1): 283-290.

A.P. Toropova, A.A. Toropov, E. Benfenati, R. Korenstein, D. Leszczynska, J. Leszczynski.
Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides.
Environ. Sci. Pollut. Res. (2015); 22: 745-757.

P.R. Duchowicz, S.E. Fioressi, D.E. Bacelo, L.M. Saavedra, A.P. Toropova, A.A. Toropov,
QSPR Studies on Refractive Indices of Structurally Heterogeneous Polymers. Chemom.
Intell. Lab. Syst. (2015); 140: 86–91.

• TO THE BEGINNING

2014

V. V. Kleandrova, F. Luan, H. González-Diaz, J. M. Ruso, A. Speck-Planche, M.N.D. S. Cordeiro,
Computational Tool for Risk Assessment of Nanomaterials: Novel QSTR-Perturbation Model for Simultaneous Prediction of Ecotoxicity and Cytotoxicity of Uncoated and Coated Nanoparticles under Multiple Experimental Conditions.
(2014) Environ. Sci. Technol., 48: 14686-14694.

Some applications of the CORAL software for nano-QSAR one can find with using the following link:
Nano Profiler 1.0
(program uploaded on 11 November 2014)

Challenges and Advances in Computational Chemistry and Physics 17. Series Editor : J. Leszczynski.
Book: Application of Computational Techniques in Pharmacy and Medicine. Edited by Leonid Gorb, Victor Kuz'min, Eugene Muratov. Springer, Nov 7, 2014 - Science - 550 pages.
The title of chapter 12: Consensus Drug Design Using It Microcosm.
Authors of chapter 12: P.M. Vassiliev, A.A. Spasov, V.A. Kosolapov, A.F. Kucheryavenko, N.A. Gurova, V.A. Anisimova

Qian Li, Xiao Ding, Hongzong Si, Hua Gao.
QSAR model based on SMILES of inhibitory rate of 2, 3-diarylpropenoic acids on AKR1C3.
(2014) Chemom. Intell. Lab. Syst., 139, 132–138.

G. Melagraki, A. Afantitis.
Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles.
(2014) RSC Adv., 4, 50713.

Liu, R., France, B., George, S., Rallo, R., Zhang, H., Xia, T., Nel, A.E., Bradley, K., Cohen, Y.
Association rule mining of cellular responses induced by metal and metal oxide nanoparticles.
(2014) Analyst, 139 (5), pp. 943-953.

F. Deng, S. Ma, M. Xie, X. Zhang, P. Li and H. Zhai.
Study on the agonists for the human Toll-like receptor-8 by molecular modeling.
(2014) Mol. BioSyst. 10, 2202.

Suresh Panneerselvam, Sangdun Choi.
Nanoinformatics: Emerging Databases and Available Tools.
(2014) Int. J. Mol. Sci.,15, pp. 7158-7182.

B. Rasulev, M. Turabekova, M. Theodor, J. Jackman, D. Leszczynska, J. Leszczynski.
Immunotoxicity of nanoparticles: Computational study suggests that CNTs and C60 fullerenes might be recognized as pathogens by Toll-like receptors.
(2014) Nanoscale, 6, pp. 3488-3495.

M. A. Turabekova, B. F. Rasulev, F. N. Dzhakhangirov, A. A. Toropov, D. Leszczynska, J. Leszczynski.
Aconitum and Delphinium Diterpenoid Alkaloids of Local Anesthetic Activity: Comparative QSAR Analysis Based on GA-MLRA/PLS and Optimal Descriptors Approach.
Journal of Environmental Science and Health, Part C, (2014); 32:213–238.

Golbamaki A., Cassano A., Lombardo A., Moggio Y., Colafranceschi M., Benfenati E.
Comparison of in silico models for prediction of Daphnia magna acute toxicity.
SAR QSAR Environ Res. (2014); 25(8):673-694.

Chapter 7, pp. 115-134 in book: Nanotoxicology: Progress toward Nanomedicine, Second Edition.
Editor by: Nancy A. Monteiro-Riviere, C. Lang Tran.
Editore: CRC Press(2014-03-07) ISBN 10: 1482203871 / ISBN 13: 9781482203875
Authors of chapter 7: Denis Fourches and Alexander Tropsha
The title of chapter 7: Quantitative Nanostructure–Activity Relationships: From Unstructured Data to Predictive Models for Designing Nanomaterials with Controlled Properties.
Nanotoxicology: Progress toward Nanomedicine

Supratik Kar, Agnieszka Gajewicz, Tomasz Puzyn, Kunal Roy, Jerzy Leszczynski.
Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles:A mechanistic QSTR approach.
(2014) Ecotoxicology and Environmental Safety, 107, pp. 162–169.

Supratik Kar, Agnieszka Gajewicz, Tomasz Puzyn, Kunal Roy.
Nano-quantitative structure–activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells.
(2014) Toxicology in Vitro, 28, pp. 600–606.

J. Veselinović, A. Veselinović, A. Toropov, A. Toropova, I.Damnjanović, G. Nikolić,
Monte Carlo Method Based QSAR Modeling of Coumarin Derivates as Potent HIV‐1 Integrase Inhibitors and Molecular Docking Studies of Selected 4‐phenyl Hydroxycoumarins.
(2014) Scientific Journal of the Faculty of Medicine in Niš 31(2), pp. 95-103.

F. Torrens, G. Castellano,
Molecular Classification of Pesticides Including Persistent Organic Pollutants, Phenylurea and Sulphonylurea Herbicides.
(2014) Molecules 19, pp. 7388-7414.

Worachartcheewan A., Mandi P., Prachayasittikul V., Toropova A.P., Toropov A.A., Nantasenamat C.
Large-scale QSAR study of aromatase inhibitors using SMILES-based descriptors.
(2014) Chemometrics and Intelligent Laboratory Systems, 138, pp. 120-126.

Karthick V., Toropova A.P., Toropov A.A., Ramanathan K.
Discovery of potential, non-toxic influenza virus inhibitor by computational techniques.
(2014) Molecular Informatics, 33 (8), pp. 559-565.

Toropova A P, Toropov A A, Benfenati E, Puzyn T, Leszczynska D, Leszczynksy J.
Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO₂ nanoparticles.
(2014) Ecotoxicology and Environmental Safety, 108, pp. 203-209.

Xiaojia He, Winfred G Aker, Jerzy Leszczynski, Huey-Min Hwang.
Using a holistic approach to assess the impact of engineered nanomaterials inducing toxicity in aquatic systems.
(2014) Journal of Food and Drug Analysis, 22(1), pp. 128-146.

Roya Kiani-Anbouhi, Mohammad Reza Ganjali, Parviz Norouzi.
Prediction of the complexation stabilities of La3+ ion with ionophores applied in lanthanoid sensors.
(2014) J Incl Phenom Macrocycl Chem ,78, pp. 325-336.

Anna Lombardo, Fabiola Pizzo, Emilio Benfenati, Alberto Manganaro, Thomas Ferrari, Giuseppina Gini.
A new in silico classification model for ready biodegradability,based on molecular fragments.
(2014) Chemosphere, 108, pp.10-16.

A. Lombardo, A. Roncaglioni, E. Benfentati, M. Nendza, H. Segner, A.Fernández, Ralph Kϋhne, A. Franco, E. Pauné, G. Schϋϋrmann.
Integrated testing strategy (ITS) for bioaccumulation assessment under REACH.
(2014) Environment International, 69, pp.40-50.

Vijay H. Masand, Andrey A. Toropov, Alla P. Toropova, Devidas T. Mahajan.
QSAR Models for Anti-Malarial Activity of 4-Aminoquinolines.
(2014) Current Computer-Aided Drug Design,10, pp.75-82.

Worachartcheewan, A., Nantasenamat, C., Isarankura-Na-Ayudhya, C., Prachayasittikul, V.
QSAR study of H1N1 neuraminidase inhibitors from influenza a virus.
(2014) Letters in Drug Design and Discovery, 11 (4), pp. 420-427.

Toropova, A.P., Toropov, A.A., Veselinović, J.B., Miljković, F.N., Veselinović, A.M.
QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method.
(2014) European Journal of Medicinal Chemistry, 77, pp. 298-305.

Achary, P.G.R.
QSPR modelling of dielectric constants of π-conjugated organic compounds by means of the CORAL software.
(2014) SAR and QSAR in Environmental Research, 25 (6), pp. 507-526.

Deng, F.-F., Xie, M.-H., Li, P.-Z., Tian, Y.-L., Zhang, X.-Y., Zhai, H.-L.
Study on the antagonists for the orphan G protein-coupled receptor GPR55 by quantitative structure-activity relationship.
(2014) Chemometrics and Intelligent Laboratory Systems, 131, pp. 51-60.

Gissi, A., Gadaleta, D., Floris, M., Olla, S., Carotti, A., Novellino, E., Benfenati, E., Nicolotti, O.
An alternative QSAR-based approach for predicting the bioconcentration factor for regulatory purposes.
(2014) Altex, 31 (1), pp. 23-36.

Toropova, A.P., Toropov, A.A., Benfenati, E., Korenstein, R.
QSAR model for cytotoxicity of SiO₂ nanoparticles on human lung fibroblasts.
(2014) Journal of Nanoparticle Research 16: 2282.

Nesměrak, K., Toropov, A.A., Toropova, A.P.
SMILES-based quantitative structure-retention relationships for RP HPLC of 1-phenyl-5-benzylsulfanyltetrazoles.
(2014) Structural Chemistry, 25 (1), pp. 311-317.

Toropov, A.A., Toropova, A.P., Raska, I., Leszczynska, D., Leszczynski, J.
Comprehension of drug toxicity: Software and databases.
(2014) Computers in Biology and Medicine, 45 (1), pp. 20-25.

Pramanik S., Roy K.
Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool "PaDEL-Descriptor".
(2014) Environmental Science and Pollution Research, 21 (4), pp. 2955-2965.

Quesada-Romero, L., Caballero, J.
Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3\upbeta β
(2014) Molecular Diversity, 18 (1), pp. 149-159.

Achary, P.G.R.
Simplified molecular input line entry system-based optimal descriptors: QSAR modelling for voltage-gated potassium channel subunit Kv7.2.
(2014) SAR and QSAR in Environmental Research, 25 (1), pp. 73-90.

Toropova, A.P., Toropov, A.A.
CORAL software: Prediction of carcinogenicity of drugs by means of the Monte Carlo method.
(2014) European Journal of Pharmaceutical Sciences, 52 (1), pp. 21-25.

Singh, K.P., Gupta, S.
Nano-QSAR modeling for predicting biological activity of diverse nanomaterials.
(2014) RSC Advances, 4 (26), pp. 13215-13230.

Feng, C., Du, X.
Theoretical models for predicting the bioconcentration factors of halogenated benzenes in fish.
(2014) Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering, 31 (1), pp. 96-102.

Gissi A., Toropov A.A., Toropova A.P., Nicolotti O., Carotti A., Benfenati E.
Building up QSAR model for toxicity of psychotropic drugs by the Monte Carlo method.
(2014) Structural Chemistry, 25 (4), pp 1067-1073.

Toropova A.P., Toropov A.A., Kudyshkin V.O., Leszczynska D., Leszczynski J.
Optimal descriptors as a tool to predict the thermal decomposition of polymers.
(2014) Journal of Mathematical Chemistry, 52 (5), pp. 1171-1181.

• TO THE BEGINNING

2013

Bingbing Sun,Ruibin Li, Xiang Wang, Tian Xia,
Predictive toxicological paradigm and high throughput approach for toxicity screening of engineered nanomaterials.
International Journal of Biomedical Nanoscience and Nanotechnology 01/2013; 3(1):4-18.

Liu, R., Rallo, R., Cohen, Y.
Quantitative Structure-Activity-Relationships for cellular uptake of nanoparticles.
(2013) Proceedings of the IEEE Conference on Nanotechnology, art. no. 6720861, pp. 154-157.

M.Nendza, S. Gabbert, R. Kϋhne, A. Lombardo, A. Roncaglioni, E. Benfenati, R. Benigni, C. Bossa, S. Strempel, M. Scheringer, A. Fernández, R. Rallo, F. Giralt, S. Dimitrov, O.Mekenyan, F. Bringezu, G. Schϋϋrmann.
A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH.
(2013) Regulatory Toxicology and Pharmacology, 66 , pp. 301–314.

H. Gonzalez-Diaz, S. Arrasate, A. Gomez-SanJuan, N.Sotomayor, E. Lete, L. Besada-Porto, J. M. Ruso.
New Theory for Multiple Input-Output Perturbations in Complex Molecular Systems. 1. Linear QSPR Electronegativity Models in Physical, Organic, and Medicinal Chemistry.
(2013) Current topics in medicinal chemistry, 13, pp.1713-1741.

Liu, R., Zhang, H.Y., Ji, Z.X., Rallo, R., Xia, T., Chang, C.H., Nel, A., Cohen, Y.
Development of structure-activity relationship for metal oxide nanoparticles.
(2013) Nanoscale, 5 (12), pp. 5644-5653.

Toropova, A.P.,Toropov, A.A.
Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO₂ nanoparticles.
(2013) Chemosphere, 93 (10), pp. 2650-2655.

Liu, R., Rallo, R., Weissleder, R., Tassa, C., Shaw, S., Cohen, Y.
Nano-SAR development for bioactivity of nanoparticles with considerations of decision boundaries.
(2013) Small, 9 (9-10), pp. 1842-1852.

Cohen, Y., Rallo, R., Liu, R., Liu, H.H.
In silico analysis of nanomaterials hazard and risk.
(2013) Accounts of Chemical Research, 46 (3), pp. 802-812.

Levet A, Bordes C, Clément Y, Mignon P, Chermette H, Marote P, Cren-Olivé C, Lantéri P.
Quantitative structure-activity relationship to predict acute fish toxicity of organic solvents.
(2013) Chemosphere, 93 (6), pp. 1094-1103.

Pizzo, F., Lombardo, A., Manganaro, A., Benfenati, E.
In silico models for predicting ready biodegradability under REACH: A comparative study.
(2013) Science of the Total Environment, 463-464, pp. 161-168.

Ahmed,L., Rasulev, B., Turabekova, M., Leszczynska, D., Leszczynski, J.
Receptor- and ligand-based study of fullerene analogues: Comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations.
(2013) Organic and Biomolecular Chemistry, 11 (35), pp.5798-5808.

Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Fanelli, R.
The definition of the molecular structure for potential anti-malaria agents by the Monte Carlo method.
(2013) Structural Chemistry, 24 (4), pp. 1369-1381.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: QSPRs of enthalpies of formation of organometallic compounds.
(2013) Journal of Mathematical Chemistry, 51 (7), pp. 1684-1693.

Alex A. Tardaguila, Jennifer C. Sy, Marielyn R. Omañada, and Eric R. Punzalan,
MLR-Based QSAR Models for Predicting Inhibitory Activity of Reverse Transcriptase by HEPT Derivatives using GETAWAY Descriptors.
KIMIKA Volume 24, Number 2, pp. 2-17 (2013).

Nesmerak, K., Toropov, A.A., Toropova, A.P., Kohoutova, P., Waisser, K.
SMILES-based quantitative structure-property relationships for half-wave potential of N-benzylsalicylthioamides.
(2013) European Journal of Medicinal Chemistry, 67, pp. 111-114.

Toropov, A.A., Toropova, A.P., Puzyn, T., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
QSAR as a random event: Modeling of nanoparticles uptake in PaCa2 cancer cells.
(2013) Chemosphere, 92 (1), pp. 31-37.

Melagraki, G., Afantitis, A.
Enalos KNIME nodes: Exploring corrosion inhibition of steel in acidic medium.
(2013) Chemometrics and Intelligent Laboratory Systems, 123, pp. 9-14.

Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., Nucci, G.D.
QSAR models for inhibitors of physiological impact of Escherichia coli that leads to diarrhea.
(2013) Biochemical and Biophysical Research Communications, 432 (2), pp. 214-225.

Toropov, A.A., Toropova, A.P., Raska Jr., I., Benfenati, E., Gini, G.
Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method.
(2013) Central European Journal of Chemistry, 11 (3), pp. 371-380.

Veselinović, A.M., Milosavljević, J.B., Toropov, A.A., Nikolić, G.M.
SMILES-based QSAR model for arylpiperazines as high-affinity 5-HT1A receptor ligands using CORAL.
(2013) European Journal of Pharmaceutical Sciences, 48 (3), pp. 532-541.

Toropova, A.P., Toropov, A.A., Martyanov, S.E., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: Monte Carlo method as a tool for the prediction of the bioconcentration factor of industrial pollutants.
(2013) Molecular Informatics, 32 (2), pp. 145-154.

Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: QSPR model of water solubility based on local and global SMILES attributes.
(2013) Chemosphere, 90 (2), pp. 877-880.

Veselinović, A.M., Milosavljević, J.B., Toropov, A.A., Nikolić, G.M.
SMILES-Based QSAR models for the calcium channel-antagonistic effect of 1,4-dihydropyridines.
(2013) Archiv der Pharmazie, 346 (2), pp. 134-139.

• TO THE BEGINNING

2012

Roca, C.P., Rallo, R., Fernandez, A., Giralt, F.
Nanoinformatics for safe-by-design engineered nanomaterials.
(2012) RSC Nanoscience and Nanotechnology, pp. 89-107.

Fernandez, A., Lombardo, A., Rallo, R., Roncaglioni, A., Giralt, F., Benfenati, E.
Quantitative consensus of bioaccumulation models for integrated testing strategies,
(2012) Environment International, 45 (1), pp. 51-58.

Zhang, H., Ji, Z., Xia, T., Meng, H., Low-Kam, C., Liu, R., Pokhrel, S., Lin, S., Wang, X., Liao, Y.-P., Wang, M., Li, L., Rallo, R., Damoiseaux, R., Telesca, D., Mädler, L., Cohen, Y., Zink, J.I., Nel, A.E.
Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation
(2012) ACS Nano, 6 (5), pp. 4349-4368.

Toropov, A.A., Toropova, A.P., Raska Jr., I., Benfenati, E., Gini, G.
QSAR modeling of endpoints for peptides which is based on representation of the molecular structure by a sequence of amino acids.
(2012) Structural Chemistry, 23 (6), pp. 1891-1904.

Thomas Vorup-Jensen, Dan Peer.
Nanotoxicity and the importance of being earnest.
Advanced Drug Delivery Reviews, 64 (2012) 1661-1662.

Toropov, A.A., Toropova, A.P., Rasulev, B.F., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: Binary classifications (active/inactive) for liver-related adverse effects of drugs.
(2012) Current Drug Safety, 7 (4), pp. 257-261.

Toropova, A.P., Toropov, A.A., Rasulev, B.F., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES.
(2012) Structural Chemistry, 23 (6), pp. 1873-1878.

Rasulev, B., Gajewicz, A., Puzyn, T., Leszczynska, D., Leszczynski, J.
Nano-QSAR: Advances and challenges.
(2012) RSC Nanoscience and Nanotechnology, pp. 220-256.

Gajewicz, A., Rasulev, B., Dinadayalane, T.C., Urbaszek, P., Puzyn, T., Leszczynska, D., Leszczynski, J.
Advancing risk assessment of engineered nanomaterials: Application of computational approaches.
(2012) Advanced Drug Delivery Reviews, 64 (15), pp. 1663-1693.

Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Puzyn, T., Leszczynska, D., Leszczynski, J.
Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli.
(2012) Chemosphere, 89 (9), pp. 1098-1102.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: Models of toxicity of binary mixtures.
(2012) Chemometrics and Intelligent Laboratory Systems, 119, pp. 39-43.

Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents.
(2012) Anti-Cancer Agents in Medicinal Chemistry, 12 (7), pp. 807-817.

Toropov, A.A., Toropova, A.P., Rasulev, B.F., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical.
(2012) Journal of Computational Chemistry, 33 (23), pp. 1902-1906.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: Quantitative models for estimating bioconcentration factor of organic compounds.
(2012) Chemometrics and Intelligent Laboratory Systems, 118, pp. 70-73.

Toropov, A.A., Toropova, A.P., Lombardo, A., Roncaglioni, A., De Brita, N., Stella, G., Benfenati, E.
CORAL: The prediction of biodegradation of organic compounds with optimal SMILES-based descriptors.
(2012) Central European Journal of Chemistry, 10 (4), pp. 1042-1048.

Mitra, I., Saha, A., Roy, K.
In silico development, validation and comparison of predictive QSAR models for lipid peroxidation inhibitory activity of cinnamic acid and caffeic acid derivatives using multiple chemometric and cheminformatics tools.
(2012) Journal of Molecular Modeling, 18 (8), pp. 3951-3967.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
The average numbers of outliers over groups of various splits into training and test sets: A criterion of the reliability of a QSPR? A case of water solubility.
(2012) Chemical Physics Letters, 542, pp. 134-137.

Lee, A., Mercader, A.G., Duchowicz, P.R., Castro, E.A., Pomilio, A.B.
QSAR study of the DPPH radical scavenging activity of di(hetero)arylamines derivatives of benzo[b]thiophenes, halophenols and caffeic acid analogues.
(2012) Chemometrics and Intelligent Laboratory Systems, 116, pp. 33-40.

Yao, L.
In silico search for drug targets of natural compounds.
(2012) Current Pharmaceutical Biotechnology, 13 (9), pp. 1632-1639.

Toropov, A.A., Nesmerak, K.
SMILES-based QSPR model for half-wave potentials of 1-phenyl-5-benzyl- sulfanyltetrazoles using CORAL.
(2012) Chemical Physics Letters, 539-540, pp. 204-208.

Roy, K., Mitra,I.
On the use of the metric r _m ² as an effective tool for validation of QSAR models in computational drug design and predictive toxicology.
(2012) Mini-Reviews in Medicinal Chemistry, 12 (6), pp. 491-504.

Toropova, A.P., Toropov, A.A., Lombardo, A., Roncaglioni, A., Benfenati, E., Gini, G.
CORAL: QSAR models for acute toxicity in fathead minnow (Pimephales promelas).
(2012) Journal of Computational Chemistry, 33 (12), pp. 1218-1223.

Mouchlis, V.D., Melagraki, G., Mavromoustakos, T., Kollias, G., Afantitis, A.
Molecular modeling on pyrimidine-urea inhibitors of TNF-α production: An integrated approach using a combination of molecular docking, classification techniques, and 3D-QSAR CoMSIA.
(2012) Journal of Chemical Information and Modeling, 52 (3), pp. 711-723.

Toropov, A.A., Toropova, A.P., Martyanov, S.E., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: Predictions of rate constants of hydroxyl radical reaction using representation of the molecular structure obtained by combination of SMILES and Graph approaches.
(2012) Chemometrics and Intelligent Laboratory Systems, 112, pp. 65-70.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G.
QSAR Models for Toxicity of Organic Substances to Daphnia magna Built up by Using the CORAL Freeware.
(2012) Chemical Biology and Drug Design, 79 (3), pp. 332-338.

Toropova, A.P., Toropov, A.A., Martyanov, S.E., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna.
(2012) Chemometrics and Intelligent Laboratory Systems, 110 (1), pp. 177-181.

Ibezim, E., Duchowicz, P.R., Ortiz, E.V., Castro, E.A.
QSAR on aryl-piperazine derivatives with activity on malaria.
(2012) Chemometrics and Intelligent Laboratory Systems, 110 (1), pp. 81-88.

• TO THE BEGINNING

2011

Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
SMILES-based QSAR approaches for carcinogenicity and anticancer activity: Comparison of correlation weights for identical SMILES attributes.
(2011) Anti-Cancer Agents in Medicinal Chemistry, 11 (10), pp. 974-982.

Garro Martinez, J.C., Duchowicz, P.R., Estrada, M.R., Zamarbide, G.N., Castro, E.A.
QSAR study and molecular design of open-chain enaminones as anticonvulsant agents.
(2011) International Journal of Molecular Sciences, 12 (12), pp. 9354-9368.

Liu, R., Rallo, R., George, S., Ji, Z., Nair, S., Nel, A.E., Cohen, Y.
Classification NanoSAR development for cytotoxicity of metal oxide nanoparticles.
(2011) Small, 7 (8), pp. 1118-1126.

Khajeh, A., Modarress, H.
Quantitative structure-property relationship prediction of liquid thermal conductivity for some alcohols.
(2011) Structural Chemistry, 22 (6), pp. 1315-1323.

Toropov, A.A., Toropova, A.P., Martyanov, S.E., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines.
(2011) Chemometrics and Intelligent Laboratory Systems, 109 (1), pp. 94-100.

Garcia, J., Duchowicz, P.R., Rozas, M.F., Caram, J.A., Mirifico, M.V., Fernandez, F.M., Castro, E.A.
A comparative QSAR on 1,2,5-thiadiazolidin-3-one 1,1-dioxide compounds as selective inhibitors of human serine proteinases.
(2011) Journal of Molecular Graphics and Modelling, 31, pp. 10-19.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
QSAR modeling of anxiolytic activity taking into account the presence of keto- and enol-tautomers by balance of correlations with ideal slopes.
(2011) Central European Journal of Chemistry, 9 (5), pp. 846-854.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: Quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats.
(2011) Journal of Computational Chemistry, 32 (12), pp. 2727-2733.

Mullen, L.M.A., Duchowicz, P.R., Castro, E.A.
QSAR treatment on a new class of triphenylmethyl-containing compounds as potent anticancer agents.
(2011) Chemometrics and Intelligent Laboratory Systems, 107 (2), pp. 269-275.

Dwivedi, N., Mishra, S., Mishra, B.N., Singh, R.B., Katoch, V.M.
3D QSAR based study of potent growth inhibitors of terpenes as Antimycobacterial agents.
(2011) Open Nutraceuticals Journal, 4, pp. 119-124.

Benfenati, E., Toropov, A.A., Toropova, A.P., Manganaro, A., Gonella Diaza, R.
CORAL software: QSAR for anticancer agents.
(2011) Chemical Biology and Drug Design, 77 (6), pp. 471-476.

Ojha, P.K., Mitra, I., Das, R.N., Roy, K.
Further exploring r_m ² metrics for validation of QSPR models.
(2011) Chemometrics and Intelligent Laboratory Systems, 107 (1), pp. 194-205.

Toropov, A.A., Toropova, A.P., Lombardo, A., Roncaglioni, A., Benfenati, E., Gini, G.
CORAL: Building up the model for bioconcentration factor and defining it's applicability domain.
(2011) European Journal of Medicinal Chemistry, 46 (4), pp. 1400-1403.

Hassan, H.M., Elnagar, A.Y., Khanfar, M.A., Sallam, A.A., Mohammed, R., Shaala, L.A., Youssef, D.T.A., Hifnawy, M.S., El Sayed, K.A.
Design of semisynthetic analogues and 3D-QSAR study of eunicellin-based diterpenoids as prostate cancer migration and invasion inhibitors.
(2011) European Journal of Medicinal Chemistry, 46 (4), pp. 1122-1130.

Toropova, A.P., Toropov, A.A., Diaza, R.G., Benfenati, E., Gini, G.
Analysis of the co-evolutions of correlations as a tool for QSAR-modeling of carcinogenicity: An unexpected good prediction based on a model that seems untrustworthy.
(2011) Central European Journal of Chemistry, 9 (1), pp. 165-174.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G.
Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: An unexpected good prediction based on a model that seems untrustworthy.
(2011) Chemometrics and Intelligent Laboratory Systems, 105 (2), pp. 215-219.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J.
CORAL: QSPR models for solubility of [C60] and [C70] fullerene derivatives.
(2011) Molecular Diversity, 15 (1), pp. 249-256.

Toropova, A.P., Toropov, A.A., Benfenati, E., Gini, G.
QSAR modelling toxicity toward rats of inorganic substances by means of CORAL.
(2011) Central European Journal of Chemistry, 9 (1), pp. 75-85.

• TO THE BEGINNING

2010

Mercader, A.G., Duchowicz, P.R., Fernandez, F.M., Castro, E.A.
Replacement method and enhanced replacement method versus the genetic algorithm approach for the selection of molecular descriptors in QSPR/QSAR theories.
(2010) Journal of Chemical Information and Modeling, 50 (9), pp. 1542-1548.

Xu Hui-Ying, Zou Jian-Wei, Hu Gui-Xiang, Wang Wei.
QSPR/QSAR models for prediction of the physico-chemical properties and biological activity of polychlorinated diphenyl ethers (PCDEs).
(2010) Chemosphere, 80(6), pp.665-670.

Toropov, A.A., Toropova, A.P., Benfenati, E.
SMILES-based optimal descriptors: QSAR modeling of carcinogenicity by balance of correlations with ideal slopes.
(2010) European Journal of Medicinal Chemistry, 45 (9), pp. 3581-3587.

Max K. Leong, Sheng-Wen Lin, Hong-Bin Chen, and Fu-Yuan Tsai,
Predicting Mutagenicity of Aromatic Amines by Various Machine Learning Approaches.
TOXICOLOGICAL SCIENCES 116(2), 498 - 513 (2010).

Toropova, A.P., Toropov, A.A., Lombardo, A., Roncaglioni, A., Benfenati, E., Gini, G.
A new bioconcentration factor model based on SMILES and indices of presence of atoms.
(2010) European Journal of Medicinal Chemistry, 45 (9), pp. 4399-4402.

Toropova, A.P., Toropov, A.A., Benfenati, E., Leszczynska, D., Leszczynski, J.
QSAR modeling of measured binding affinity for fullerene-based HIV-1 PR inhibitors by CORAL.
(2010) Journal of Mathematical Chemistry, 48 (4), pp. 959-987.