Predictive Models for Kinetic Parameters of Cycloaddition Reactions. Issue 1 (22nd August 2018)

• Record Type: Journal Article
• Authors: Glavatskikh, Marta; Madzhidov, Timur; Horvath, Dragos; Nugmanov, Ramil; Gimadiev, Timur; Malakhova, Daria; Marcou, Gilles; Varnek, Alexandre
• Is Part Of: Molecular informatics. Volume 38:Issue 1/2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-22
• Subjects: cycloaddition reactions -- QSPR -- Condensed Graph of Reaction -- Generative Topographic Mapping; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201800077
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: This paper reports SVR (Support Vector Regression) and GTM (Generative Topographic Mapping) modeling of three kinetic properties of cycloaddition reactions: rate constant (log k ), activation energy (Ea) and pre‐exponential factor (logA). A data set of 1849 reactions, comprising (4+2), (3+2) and (2+2) cycloadditions (CA) were studied in different solvents and at different temperatures. The reactions were encoded by the ISIDA fragment descriptors generated for Condensed Graph of Reaction (CGR). For a given reaction, a CGR condenses structures of all the reactants and products into one single molecular graph, described both by conventional chemical bonds and "dynamical" bonds characterizing chemical transformations. Different scenarios of log k assessment were exploited: direct modeling, application of the Arrhenius equation and temperature‐scaled GTM landscapes. The log k models with optimal cross‐validated statistics (Q 2 =0.78–0.94 RMSE=0.45–0.86) have been challenged to predict rates for the external test set of 200 reactions, comprising both reactions that were not present in the training set, and training set transformations performed under different reaction conditions. The models are freely available on our web‐server: http://cimm.kpfu.ru/models. Abstract :