CORAL can be used in scientific studies.
Here you can find a list of scientific papers in accordance with dates which used or cited CORAL.
If you wish to add your scientific publications where CORAL descriptors were used, please contact us.

2017

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

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. Journal of Physics Condensed Matter, Accepted Manuscript online 13 June 2017. https://doi.org/10.1088/1361-648X/aa7933

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, Available online 29 May 2017. DOI: 10.1016/j.ejps.2017.05.061

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, Accepted for publication May 22, 2017. DOI: 10.2174/1570163814666170525114128

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. DOI: 10.1016/j.etap.2017.05.011

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. https://doi.org/10.1371/journal.pcbi.1005372

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.

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 xxx (2017) 1-8. Available online 5 April 2017. http://doi.org/10.1016/j.fct.2017.04.003

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

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, Available online 30 March 2017. DOI: 10.1016/j.fct.2017.03.060

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.

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

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-OFAR: 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. doi:10.4018/IJQSPR.2017010102

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. DOI:10.2174/1389200218666170301105916

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; CORAL: QSAR models for bee toxicity. Toxicological & Environmental Chemistry; Published online: 17 Oct 2016, 1-12. DOI: 10.1080/02772248.2016.1242006

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

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.

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. (2016), Phase behavior of blends of PCBM with amorphous polymers with different aromaticity. J. Polym. Sci. Part B: Polym. Phys., 54: 994-1001.

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. 2016. Quasi-QSPR to Predict Proteins Behavior Under Various Concentrations of Drug Using Nanoconductometric Assay. NanoWorld J. 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.

C. Leone, S. Manganelli, A. Golbamaki, R. Korenstein, P. Bigini, and E. Benfenati, "Parameters influencing the cytotoxicity of nanoparticles". (2016) review submitted in July 2016 to Nanotoxicology. Article in press

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., 2016. 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), 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 Carbo-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.

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

Lalitha Simon, Abdelli Imane, K. K. Srinivasan, Lokesh 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. 2016, pp. 1-14. doi:10.1007/s12539-016-0169-4

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.

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.

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.

Xiaojia He, Winfred G. Aker, Peter P. Fu, Huey-Min Hwang. Toxicity of engineered metal oxide nanomaterials mediated by nano–bio–eco–interactions: a review and perspective. Environ. Sci.: Nano, 2015, 2, 564-582.

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.

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, ( 2015 ) 5(5): 429-436.

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 Rudén, 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 (2015) Combinatorial Chemistry and High Throughput Screening, 18 (4), 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.

H. Zhu, W. Guo, Z. Shen, Q. Tang, W.Ji, L. Jia. QSAR models for degradation of organic pollutants in ozonation process under acidic condition. Chemosphere. (2015); 119: 65–71.

Chapter in Book
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. Veselinovic´, 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.

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.

Kiani-Anbouhi, R., Ganjali, M.R., Norouzi, P. Prediction of the complexation stabilities of La3+ ion with ionophores applied in lanthanoid sensors.(2014) Journal of Inclusion Phenomena and Macrocyclic Chemistry, 78 (1-4), pp. 325-336.

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.

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.

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.

Singh, S., Supuran, C.T. Chemometric QSAR modeling and in silico design of carbonic anhydrase inhibition of a coral secretory isoform by sulfonamide. (2013) Bioorganic and Medicinal Chemistry, 21 (6), pp. 1495-1502.

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.

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.

Zhu, W., Liu, Y., Zhai, X., Wang, X., Zhu, Y., Wu, D., Zhou, H., Gong, P., Zhao, Y. Design, synthesis and 3D-QSAR analysis of novel 2-hydrazinyl-4- morpholinothieno[3,2-d]pyrimidine derivatives as potential antitumor agents. (2012) European Journal of Medicinal Chemistry, 57, pp. 162-175.

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.

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.

Hsing, M., Byler, K.,Cherkasov, A. Prediction of highly-connected hubs in protein interaction networks by QSAR and biological data descriptors. (2011) Protein-Protein Interactions, pp. 75-102.

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.

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.