PUBLICATIONS

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CORAL software-project publications:
Scholar_Google/ Citations/ List_Works


The coronavirus COVID-19

Andrey A. Toropov, Alla P. Toropova, Aleksandar Veselinovic, Danuta Leszczynska, Jerzy Leszczynski, SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model. Journal of Biomolecular Structure and Dynamics, Accepted Aug 29, 2020. Published online: 09 Sep 2020. DOI: 10.1080/07391102.2020.1818627

Correlation Intensity Index (CII) is a new criterion of predictive potential of a QSPR/ QSAR model

Alla P. Toropova and Andrey A. Toropov, Use of the Monte Carlo Method to Build up QSPR/QSAR Models: Index of Ideality of Correlation and Correlation Intensity Index. Chapter 3(pp.111-156), 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/

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

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

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

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

S. Ahmadi, A.P. Toropova, and A.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

Correlation Contradictions Index (CCI) is a new criterion of predictive potential of a QSPR/ QSAR model

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.

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.

Index of Ideality of Correlation (IIC) is a new criterion of predictive potential of a QSPR/ QSAR model

A.P. Toropova, A.A. Toropov, D. Leszczynska, J. 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

A.P. Toropova, P.R. Duchowicz, L.M. Saavedra, E.A. Castro, A.A. Toropov, The use of the index of ideality of correlation to build up models for bioconcentration factor. Molecular Informatics, 2020, 39, 1900070.

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.

A.A. Toropov, A.P. Toropova, E. Benfenati, QSAR model for pesticides toxicity to Rainbow Trout based on "ideal correlations". Aquatic Toxicology, 227, 2020, 105589.

A.A. Toropov, I. Raška Jr., A.P. Toropova, M. Raškova, A.M. Veselinovic, J.B. Veselinovic, 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.

Alla P. Toropova, Andrey A. Toropov, Does the index of ideality of correlation detect the better model correctly? Molecular Informatics, 2019, 38, 1800157.

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).

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, Use of The Index of Ideality of Correlation to improve predictive potential for biochemical endpoints. Toxicology Mechanisms and Methods, 2019, 29:1, 43-52.

Toropova, A.P.; Toropov, A.A.; Veselinović, A.M.; Veselinović, J.B.; Leszczynska, D.; Leszczynski, J. Semi-correlations combined with the Index of Ideality of Correlation: a tool to build up model of mutagenic potential. Mol. Cell. Biochem., 2019, Volume 452, Issue 1-2, pp. 133-140.

Toropova, A.P.; Toropov, A.A. Use of the Index of Ideality of Correlation to improve models of eco-toxicity. Environ. Sci. Pollut. Res., 2018, 25(31), pp. 31771-31775.

Toropova, A.P.; Toropov, A.A.; Leszczynska, D.; Leszczynski, J. The Index of Ideality of Correlation: hierarchy of Monte Carlo models for glass transition temperatures of polymers. J. Polym. Res., 2018, 25(10), art. no. 221.

P. Kumar, A. Kumar & J. Sindhu, 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, (2019) 30:2, 63-80.

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). 2018, doi: 10.1055/a-0652-5290

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 (2018) Computational Biology and Chemistry, 75, pp. 32-38.

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 (2018) Structural Chemistry, 29 (2), pp. 441-449.

Toropov, A.A.; Carbó-Dorca, R.; Toropova, A.P. Index of Ideality of Correlation: new possibilities to validate QSAR: a case study. Struct. Chem., 2018, 29(1), 33-38.

Toropov, A.A.; Toropova, A.P. The Index of Ideality of Correlation: A criterion of predictive potential of QSPR/QSAR models? Mut. Res. Gen. Tox. En. Mut., 2017, 819, 31-37.

Toropova, A.P.; Toropov, A.A. The Index of Ideality of Correlation: A criterion of predictability of QSAR models for skin permeability? Sci. Total Environ., 2017, 586, 466-472.



QSPR/QSAR as a random event

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.

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.

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.

A. P. Toropova, A.A. Toropov, J. B. Veselinovic, A. M. Veselinovic. QSAR as a random event: a case of NOAEL. Environ. Sci. Poll. Res. (2015), 22(11), 8264-8271.

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.

Toropov A.A., Toropova A.P., Puzyn T., Benfenati E., Gini G., Leszczynska D., Leszczynksy J. QSAR as a random event: Models for nanoparticles uptake in PaCa2 cancer cells. Chemosphere 2013; 92: 31–37.



QSPR/QSAR analyses of Nano materials

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

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

S. Ahmadi, A.P. Toropova, and A.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

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

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

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

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

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

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

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)

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.

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.

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. DOI: 10.1016/j.ecoenv.2017.01.054

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. DOI: 10.1016/j.jtbi.2017.01.012

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

Alla P. Toropova and Andrey A. Toropov, Assessment of nano-QSPR models of organic contaminant absorption by carbon nanotubes for ecological impact studies. Materials Discovery, 4 (2016) 22–28. DOI:10.1016/j.md.2016.03.003

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

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.

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.

Manganelli, S., Leone, C., Toropov, A.A., Toropova, A.P., Benfenati, E. (2015): QSAR model for cytotoxicity of silica nanoparticles on human embryonic kidney cells. Materials Today: Proceedings, Volume 3, Issue 3, 2016, Pages 847-854.

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.

Alla P. Toropova, Andrey A. Toropov. QSPR model for dispersibility of graphene in various solvents, Letters in Drug Design & Discovery, 13( 6), 2016, 514-520.

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

Toropova, A.P., Toropov, A.A., Rallo, R., Leszczynska, D., and Leszczynski, J., Nano-QSAR: Genotoxicity of multi-walled carbon nanotubes. Int. J. Environ. Res., 10(1): 59-64, Winter 2016.

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, A. A. Toropov, Quasi-SMILES and nano-QFAR: United model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere, 139 (2015) 18-22.

A. A. Toropov, R. Rallo, A. 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.

A. P. Toropova and A. A. Toropov. Mutagenicity: QSAR - quasi-QSAR - nano-QSAR. Mini-Reviews in Medicinal Chemistry, 2015, 15( 2): 608-621

A. A. Toropov, A. P. Toropova, A. M. Veselinović, J. B. Veselinović, K. Nesměrák, 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.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.


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

A. A. Toropov, A.P. Toropova. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere (2015) 124: 40–46.

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.(2015) 26(1); 29-40.

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: 203–209.

Toropova A.P., Toropov A.A., Benfenati E., Korenstein R., QSAR model for cytotoxicity of SiO2 nanoparticles on human lung fibroblasts, Journal of Nanoparticle Research 2014; 16: 2282.

Toropov A.A., Toropova A. P., Optimal descriptor as a translator of eclectic data into endpoint prediction: Mutagenicity of fullerene as a mathematical function of conditions. Chemosphere, 2014; 104: 262-264.

Toropov A A, Toropova A P, Puzyn T, Benfenati E, Gini G, Leszczynska D, Leszczynksy J., QSAR as a random event: Models for nanoparticles uptake in PaCa2 cancer cells. Chemosphere 2013; 92 : 31–37.

A.P. Toropova, A.A. Toropov, T. Puzyn, E. Benfenati, D. Leszczynska, J. Leszczynski, Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of Micro-Electro-Mechanical Systems. J. Math. Chem. 2013; 51: 2230-2237.

A. P. Toropova, A. A. Toropov, Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO2 nanoparticles. Chemosphere. 2013; 93:2650–2655.

A.A. Toropov, A.P. Toropova, I. Raska Jr., E. Benfenati, G. Gini , Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method. Central European Journal of Chemistry, 11 (2013)371-380.

A.A. Toropov, A.P. Toropova, E. Benfenati, G. Gini, T. Puzyn, D.Leszczynska, J. Leszczynski,  Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli. Chemosphere 89 (2012) 1098-1102.

T. Puzyn, B. Rasulev, A. Gajewicz, X. Hu, T.P. Dasari, A. Michalkova, H.-M. Hwan , A. Toropov , D. Leszczynska , J. Leszczynski,  Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nat.Nanotechnol. 6 (2011) 175-178.

A.P. Toropova, A.A. Toropov,  E. Benfenati,  G. Gini,  D.Leszczynska,  J.Leszczynski,  CORAL:QSPR models for solubility of [C60] and [C70] fullerene derivatives. Mol. Divers. 15 (2011) 249-256.

T. Petrova, B.F. Rasulev, A.A. Toropov, D. Leszczynska, J.Leszczynski, Improved model for fullerene C 60 solubility in organic solvents based on quantum-chemical and topological descriptors. Journal of Nanoparticle Research 13 (2011) 3235-3247.

A.A. Toropov,  A.P. Toropova,  E. Benfenati,  D. Leszczynska,  J.Leszczynski , SMILES Based Optimal Descriptors: QSAR Analysis of Fullerene-Based HIV-1 PR Inhibitors by Means of Balance of Correlations. J. Comput. Chem. 31(2010) 381-392.

A.P. Toropova, A.A.Toropov, E. Benfenati, G.Gini, D. Leszczynska, J.Leszczynski , QSAR modeling of measured binding affinity for fullerene-based HIV-1 PR inhibitors by CORAL. J.Math. Chem. 47 (2010)959-987.

A.A. Toropov,  A.P. Toropova,  E. Benfenati,  D. Leszczynska,  J.Leszczynski , Additive InChI-based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents. J. Math. Chem. 46 (2009)1232-1251.

A.A. Toropov, B.F. Rasulev, D. Leszczynska, J. Leszczynski, Multiplicative SMILES-based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents. Chemical Physics Letters 457 (4-6) (2008) 332-336.

A.A. Toropov, B.F. Rasulev, D. Leszczynska, J. Leszczynski, Additive SMILES based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents. Chemical Physics Letters 444 (1-3)(2007) 209-214.

A.A. Toropov, D.Leszczynska, J. Leszczynski, Predicting thermal conductivity of nanomaterials by correlation weighting technological attributes codes. Materials Letters 61 (26) (2007)4777-4780.

A.A. Toropov, D.Leszczynska,J. Leszczynski , QSPR study on solubility of fullerene C60 in organic solvents using optimal descriptors calculated with SMILES. Chemical Physics Letters 441 (1-3) (2007)119-122.

A.A. Toropov,  D.Leszczynska,  J. Leszczynski,  Predicting water solubility and octanol water partition coefficient for carbon nanotubes based on the chiral vector. Computational Biology and Chemistry 31 (2)(2007) 127-128.

A.A. Toropov, J. Leszczynski, A new approach to the characterization of nanomaterials: Predicting Young's modulus by correlation weighting of nanomaterials codes. Chemical Physics Letters 433 (1-3) (2006) 125-129.

Chapters in Book:

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, In book: "Multi-Scale Approaches in Drug Discovery: From Synthetic Methodologies and Biological Assays to In Silico Experiments and Back". Edited: Speck-Planche A. Elsevier Science & Technology Books, 01 mar 2017 - 230 pagine. ISBN: 0081011296, 9780081011294

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

Toropov A.A., Toropova A.P., Nesměrák 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

A.A.Toropov, B.F. Rasulev, D. Leszczynska and J. Leszczynski, New Approach to QSPR Modeling of Fullerene C60 Solubility in Organic Solvents: An Application of SMILES-Based Optimal Descriptors 
Chapter 14, pp. 337-350 in Book: Medicinal Chemistry and Pharmacological Potential of Fullerenes and Carbon Nanotubes, Edited by F. Cataldo  and T.Da Ros http://rd.springer.com/book/10.1007%2F978-1-4020-6845-4


The CORAL software: SMILES representation

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.

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.

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.

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 . Chemometrics and Intelligent Laboratory Systems, (2014) 138: 120-126.

Toropova A.P., Toropov A A, CORAL software: Prediction of Carcinogenicity of Drugs by means of The Monte Carlo method. European Journal of Pharmaceutical Sciences, (2014) 52: 21-25.

Nesměrák K., Toropov A.A, Toropova A.P., Kohoutova P., Waisser K.: SMILES-based quantitative structure-property relationships for half-wave potential of N-benzylsalicylthioamides. European Journal of Medicinal Chemistry, (2013) 67: 111-114.

A.P. Toropova, A.A. Toropov, E. Benfenati, G.Gini, D. Leszczynska, J.Leszczynski, CORAL: Quantitative Structure - Activity Relationship Models for Estimating Toxicity of Organic Compounds in Rats. J. Comput. Chem. 32 (2011) 2727-2733.

A.P. Toropova, A.A. Toropov, E. Benfenati, G. Gini, QSAR modelling toxicity toward rats of inorganic substances by means of CORAL. Cent. Eur. J. Chem. 9 (2011) 75-85.

A.P.Toropova, A.A. Toropov, R. Gonella Diaza, E. Benfenati, G. Gini, Analysis of the co-evolutions of correlations as a tool for QSAR-modeling carcinogenicity: an unexpected good prediction based on a model that seems untrustworthy. Cent. Eur. J. Chem 9 (2011) 165-174.

E. Benfenati, A.A. Toropov, A.P. Toropova, A. Manganaro, R. GonellaDiaza, CORAL Software: QSAR for Anticancer Agents. Chem. Biol. Drug Des.77 (2011) 471-476.

A.P. Toropova, A.A. Toropov, E. Benfenati, G. Gini, Co-evolutions of correlations for QSAR of toxicity of organometallic and inorganic substances: an unexpected good prediction based on a model that seems untrustworthy. Chemometr. Intell. Lab. 105 (2011) 215-219.

A.A.Toropov, A.P. Toropova, A. Lombardo, A. Roncaglioni, E.Benfenati,G.Gini, CORAL: building up the model for bioconcentration factor and defining it's applicability domain. Eur. J. Med. Chem. 46 (2011) 1400-1403.



The CORAL software: representation by SMILES and Graph

A. P. Toropova, A. A. Toropov, J. B. Veselinovic´, F. N. Miljkovic´, A. M. Veselinovic´, QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method. European Journal of Medicinal Chemistry, (2014); 77: 298–305.

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.

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.

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.

A.P. Toropova, A.A. Toropov, S.E. Martyanov, E. Benfenati, G. Gini, D. Leszczynska, J. Leszczynski, CORAL: QSAR modeling of toxicity of organic chemicals towards Daphnia magna. Chemometrics and Intelligent Laboratory Systems 110 (2012) 177-181.

A.A. Toropov, A.P. Toropova, S.E. Martyanov, E. Benfenati, G. Gini, D. Leszczynska, J. Leszczynski, Comparison of SMILES and molecular graphs as the representation of the molecular structure for QSAR analysis for mutagenic potential of polyaromatic amines. Chemometrics and Intelligent Laboratory Systems 109 (2011) 94-100.


Optimal descriptors which were calculated with SMART

A. A.Toropov, A. P. Toropova, E. Benfenati, A. Manganaro, QSPR modelling of enthalpies of formation for organometallic compounds by SMART-based optimal descriptors. J. Comput. Chem. 30(2009) 2576-2582.


Optimal descriptors which were calculated with InChI

A.P. Toropova, A.A. Toropov, E. Benfenati, G. Gini, Simplified Molecular Input-Line Entry System and International Chemical Identifier in the QSAR Analysis of Styrylquinoline Derivatives as HIV-1 Integrase Inhibitors. Chemical Biology and Drug Design 77 (2011) 343-360.

A.A. Toropov, A.P. Toropova , E. Benfenati, D. Leszczynska , J.Leszczynski, InChI-based optimal descriptors: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors by correlation balance.European Journal of Medicinal Chemistry 45 (2010) 1387-1394.

A.A. Toropov, A.P. Toropova, E. Benfenati, QSAR-modelingof toxicity of organometallic compounds by means of the balance of correlations for InChI-based optimal descriptors.Molecular Diversity 14 (2010) 183-192.

A.A. Toropov, A.P. Toropova, E. Benfenati, QSPR modeling of octanol water partition coefficient of platinum complexes by InChI-based optimal descriptors. Journal of Mathematical Chemistry 46 (2009) 1060-1073.

A.A. Toropov, A.P. Toropova, E. Benfenati, D. Leszczynska, J.Leszczynski, Additive InChI-based optimal descriptors: QSPR modeling of fullerene C60 solubility in organic solvents. Journal of Mathematical Chemistry 46 (2009) 1232-1251.

A.A. Toropov, A.P. Toropova, E. Benfenati, D. Leszczynska, J.Leszczynski, Use of the international chemical identifier for constructing QSPR-model of normal boiling points of acyclic carbonyl substances. Journal of Mathematical Chemistry 47 (2009) 355-369.


Drug Design // Drug Discovery

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

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.

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.

J. B. Veselinovic´, A. A. Toropov, A.P. Toropova, G. M. Nikolic´, A. M. Veselinovic´. Monte Carlo Method-Based QSAR Modeling of Penicillins Binding to Human Serum Proteins. Arch. Pharm., (2015) 348(1), 62-67.

A. A. Toropov, J. B. Veselinovic´, A.r M. Veselinovic´, F. N. Miljkovic´, 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.

Nieves C. Comelli, Erlinda V. Ortiz, Magdalena Kolacz, Alla P. Toropova, Andrey A. Toropov, Pablo R. Duchowicz, Eduardo A. Castro, Conformation-Independent QSAR on c-Src Tyrosine Kinase Inhibitors. Chemometrics and Intelligent Laboratory Systems , (2014) 134:47–52.

Vijay H. Masand, Andrey A. Toropov, Alla P. Toropova, Devidas T. Mahajan, QSAR models for anti-malarial activity of 4-aminoquinolines. Current Computer-Aided Drug Design, (2014), 10: 75-82.

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 . Chemometrics and Intelligent Laboratory Systems, (2014) 138: 120-126.

Karthick V., Toropova A.P., Toropov A.A., Ramanathan K. Discovery of potential, non-toxic influenza virus inhibitor by computational techniques. Molecular Informatics, (2014) 33 (8): 559-565.

Toropova A.P., Toropov A. A., CORAL software: Prediction of Carcinogenicity of Drugs by means of The Monte Carlo method. European Journal of Pharmaceutical Sciences, ( 2014) 52: 21-25.

Toropov A. A., Toropova A .P., Raska I. Jr., Benfenati E., Gini G. Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method . Central European Journal Chemistry, (2013) 11 : 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. Eur. J. Pharm. Sci., (2013) 48 : 532-541.

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. Archiv der Pharmazie, (2013) 346(2) : 134-139.


Physicochemical properties // QSPR

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.

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.

Andrey A. Toropov, Alla P. Toropova, Claudia Ileana Cappelli, Emilio Benfenati. CORAL: model for octanol/water partition coefficient. Fluid Phase Equilibria, 2015; 397, 44-49.

A.P. Toropova, A.A. Toropov, E. Benfenati, G.Gini, D. Leszczynska, J. Leszczynski CORAL: QSPRs of enthalpies of formation of organometallic compounds, J. Math. Chem., (2013) 51: 1684-1693.

Nesměrák K., Toropov A.A, Toropova A.P., Kohoutova P., Waisser K.: SMILES-based quantitative structure-property relationships for half-wave potential of N-benzylsalicylthioamides. European Journal of Medicinal Chemistry, (2013) 67: 111-114.

A.P. Toropova, A.A. Toropov, T. Puzyn, E. Benfenati, D. Leszczynska, J. Leszczynski, Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of Micro-Electro-Mechanical Systems J. Math. Chem. , (2013) 51: 2230-2237.

A.P. Toropova, A. A. Toropov, E. Benfenati, G. Gini, D. Leszczynska, J. Leszczynski, 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. Chemical Physics Letters, (2012) 542: 134-137.

A.P. Toropova, A. A. Toropov, E. Benfenati, G. Gini, D. Leszczynska, J. Leszczynski, CORAL: QSPR models for solubility of [C60] and [C70] fullerene derivatives. Mol. Divers. (2011) 105: 249-256.

A.A. Toropov, A. P. Toropova, I. Raska , E. Benfenati QSPR modeling of octanol/water partition coefficient of antineoplastic agents by balance of correlations. European Journal of Medicinal Chemistry, (2010) 45:1639–1647.

A.A. Toropov, A. P. Toropova, E. Benfenati, A. Manganaro, QSPR modelling of enthalpies of formation for organometallic compounds by SMART-based optimal descriptors. J. Comput. Chem., (2009) 30: 2576-2582.


Eco-toxicological group // Risk Assessment

Jovana B. Veselinovic, Aleksandar M. Veselinovic, 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, 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.

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.

A. A. Toropov, A.P. Toropova. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere (2015) 124: 40–46

A. A. Toropov, A.P. Toropova, Optimal descriptor as a translator of eclectic data into endpoint prediction: Mutagenicity of fullerene as a mathematical function of conditions. Chemosphere (2014) 104: 262-264.

A.P. Toropova, A.A. Toropov, , S.E. Martyanov , E. Benfenati , G. Gini , D. Leszczynska , J. Leszczynksy , CORAL: Monte Carlo method as a tool for the prediction of the bioconcentration factor of industrial pollutants. Mol .Inform. (2013) 32 : 145-154.

A.P. Toropova, A.A. Toropov, E. Benfenati, G. Gini, D. Leszczynska, J. Leszczynski, Coral: Quantitative model for estimating bioconcentration factor of organic compounds. Chemometr. Intell. Lab. (2012) 118: 70-73.

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.

A. A. Toropov, A.P. Toropova, I. Raska Jr, D. Leszczynska , J. Leszczynksi , Comprehension of drug toxicity: Software and databases. Computers in Biology and Medicine. (2014) 45: 20-25.

A. A. Toropov, A.P. Toropova, E. Benfenati, G.Gini, D. Leszczynska, J. Leszczynski, G. De Nucci , QSAR models for inhibitors of physiological impact of Escherichia coli that leads to diarrhea. Biochem Biophys Res Commun. ( 2013) 432 : 214-225.

A.P. Toropova, A. A. Toropov, E. Benfenati, G. Gini, D. Leszczynska, J. Leszczynski, CORAL: Models of toxicity of binary mixtures. Chemometrics Intelligent Laboratory System 119 (2012) 39-43.

A.P. Toropova, A.A. Toropov, A.Lombardo, A. Roncaglioni, E. Benfenati, G.Gini, CORAL: QSAR model for acute toxicity in Fathead Minnow (Pimephales promelas). J. Comput. Chem. (2012) 33: 1218-1223.

A.P. Toropova, A.A. Toropov, E. Benfenati, G.Gini, D. Leszczynska, J. Leszczynski, CORAL: Quantitative Structure–Activity Relationship Models for Estimating Toxicity of Organic Compounds in Rats. J. Comput. Chem. (2011) 32: 2727-2733.

A.P. Toropova, A.A. Toropova, E. Benfenati, G. Gini, QSAR modelling toxicity toward rats of inorganic substances by means of CORAL Cent. Eur. J. Chem. (2011 ) 9(1) : 75-85.