J Chem Inf Model - Modeling and benchmark data set for the inhibition of c-Jun N-terminal kinase-3.

Tópicos

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Resumo

The goal of this paper is to present and describe a novel 2D- and 3D-QSAR (quantitative structure-activity relationship) binary classification data set for the inhibition of c-Jun N-terminal kinase-3 with previously unpublished activities for a diverse set of compounds. JNK3 is an important pharmaceutical target because it is involved in many neurological disorders. Accordingly, the development of JNK3 inhibitors has gained increasing interest. 2D and 3D versions of the data set were used, consisting of 313 (70 actives) and 249 (60 actives) compounds, respectively. All compounds, for which activity was only determined for the racemate, were removed from the 3D data set. We investigated the diversity of the data sets by an agglomerative clustering with feature trees and show that the data set contains several different scaffolds. Furthermore, we show that the benchmarks can be tackled with standard supervised learning algorithms with a convincing performance. For the 2D problem, a random decision forest classifier achieves a Matthew's correlation coefficient of 0.744, the 3D problem could be modeled with a Matthew's correlation coefficient of 0.524 with 3D pharmacophores and a support vector machine. The performance of both data sets was evaluated within a nested 10-fold cross-validation. We therefore suggest that the data set is a reasonable basis for generating QSAR models for JNK3 because of its diverse composition and the performance of the classifiers presented in this study.

Resumo Limpo

goal paper present describ novel d dqsar quantit structureact relationship binari classif data set inhibit cjun ntermin kinas previous unpublish activ divers set compound jnk import pharmaceut target involv mani neurolog disord accord develop jnk inhibitor gain increas interest d d version data set use consist activ activ compound respect compound activ determin racem remov d data set investig divers data set agglom cluster featur tree show data set contain sever differ scaffold furthermor show benchmark can tackl standard supervis learn algorithm convinc perform d problem random decis forest classifi achiev matthew correl coeffici d problem model matthew correl coeffici d pharmacophor support vector machin perform data set evalu within nest fold crossvalid therefor suggest data set reason basi generat qsar model jnk divers composit perform classifi present studi

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