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Volume 21
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Article

Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions

by
Assima Rakhimbekova
1,
Timur I. Madzhidov
1,*,
Ramil I. Nugmanov
1,
Timur R. Gimadiev
2,
Igor I. Baskin
1,3,4 and
Alexandre Varnek
2,4,*
1
A.M. Butlerov Institute of Chemistry, Kazan Federal University, 420008 Kazan, Russia
2
Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo 001-0021, Japan
3
Faculty of Physics, Moscow State University, 119234 Moscow, Russia
4
Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, 67000 Strasbourg, France
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(15), 5542; https://doi.org/10.3390/ijms21155542
Submission received: 13 July 2020 / Revised: 27 July 2020 / Accepted: 30 July 2020 / Published: 3 August 2020
(This article belongs to the Special Issue QSAR and Chemoinformatics in Molecular Modeling and Drug Design)
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Abstract

Nowadays, the problem of the model’s applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models’ performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several “best” AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem.
Keywords: applicability domain; Quantitative Reaction–Property Relationship; QSAR/QSPR; chemical reactions; chemoinformatics; machine learning; reaction mining applicability domain; Quantitative Reaction–Property Relationship; QSAR/QSPR; chemical reactions; chemoinformatics; machine learning; reaction mining

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MDPI and ACS Style

Rakhimbekova, A.; Madzhidov, T.I.; Nugmanov, R.I.; Gimadiev, T.R.; Baskin, I.I.; Varnek, A. Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions. Int. J. Mol. Sci. 2020, 21, 5542. https://doi.org/10.3390/ijms21155542

AMA Style

Rakhimbekova A, Madzhidov TI, Nugmanov RI, Gimadiev TR, Baskin II, Varnek A. Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions. International Journal of Molecular Sciences. 2020; 21(15):5542. https://doi.org/10.3390/ijms21155542

Chicago/Turabian Style

Rakhimbekova, Assima, Timur I. Madzhidov, Ramil I. Nugmanov, Timur R. Gimadiev, Igor I. Baskin, and Alexandre Varnek. 2020. "Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions" International Journal of Molecular Sciences 21, no. 15: 5542. https://doi.org/10.3390/ijms21155542

APA Style

Rakhimbekova, A., Madzhidov, T. I., Nugmanov, R. I., Gimadiev, T. R., Baskin, I. I., & Varnek, A. (2020). Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions. International Journal of Molecular Sciences, 21(15), 5542. https://doi.org/10.3390/ijms21155542

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