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Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Review Article

The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR

Author(s): Andrey A. Toropov* and Alla P. Toropova

Volume 16, Issue 3, 2020

Page: [197 - 206] Pages: 10

DOI: 10.2174/1573409915666190328123112

Price: $65

Abstract

Background: The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints.

Methods: Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model.

Results: The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated.

Conclusion: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Keywords: QSPR/QSAR, monte carlo method, CORAL software, index of ideality of correlation, optimal descriptors, physicochemical.

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Toropov, A.A.; Toropova, A.P. Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes. Chem. Phys. Lett., 2018, 701, 137-146.
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Toropov, A.A.; Toropova, A.P. Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere, 2015, 124(1), 40-46.
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Toropova, A.P.; Toropov, A.A. Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO(2) nanoparticles. Chemosphere, 2013, 93(10), 2650-2655.
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Toropov, A.A.; Toropova, A.P. Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere, 2015, 139, 18-22.
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Toropova, A.P.; Toropov, A.A. Assessment of nano-QSPR models of organic contaminant absorption by carbon nanotubes for ecological impact studies. Materials Discovery, 2016, 4, 22-28.
[http://dx.doi.org/10.1016/j.md.2016.03.003]
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Toropov, A.A.; Toropova, A.P.; Raska, I., Jr; 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. Struct. Chem., 2012, 23(6), 1891-1904.
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[http://dx.doi.org/10.1016/j.compbiolchem.2015.09.009] [PMID: 26454621]
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Toropova, A.P.; Toropov, A.A.; Beeg, M.; Gobbi, M.; Salmona, M. Utilization of the Monte Carlo method to build up QSAR models for hemolysis and cytotoxicity of antimicrobial peptides. Curr. Drug Discov. Technol., 2017, 14(4), 229-243.
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Toropova, A.P.; Toropov, A.A.; Benfenati, E. Semi-correlations as a tool to build up categorical (active/inactive) model of GABAA receptor modulator activity (2019). Struct. Chem., 2019, 30(3), 853-861.
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Toropova, A.P.; Toropov, A.A. CORAL: Binary classifications (active/inactive) for drug-induced liver injury. Toxicol. Lett., 2017, 268, 51-57.
[http://dx.doi.org/10.1016/j.toxlet.2017.01.011] [PMID: 28111161]
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Toropova, A.P.; Toropov, A.A.; Benfenati, E.; Gini, G.; Leszczynska, D.; Leszczynski, J. CORAL: Models of toxicity of binary mixtures. Chemom. Intell. Lab. Syst., 2012, 119, 39-43.
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Duchowicz, P.R.; Bacelo, D.E.; Fioressi, S.E.; Palermo, V.; Ibezim, N.E.; Romanelli, G.P. QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors. Med. Chem. Res., 2018, 27(2), 420-428.
[http://dx.doi.org/10.1007/s00044-017-2069-5]
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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), 221.
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Toropov, A.A.; Toropova, A.P. The Index of Ideality of Correlation: A criterion of predictive potential of QSPR/QSAR models? In: Mut. Res. Gen. Tox. En. Mut; , 2017; 819, pp. 31-37.
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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.
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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.
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Toropova, A.P.; Toropov, A.A. Use of the index of ideality of correlation to improve models of eco-toxicity. Environ. Sci. Pollut. Res. Int., 2018, 25(31), 31771-31775.
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