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Classification of Metal Binders by Naıve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modelingстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Web of Science,
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 12 февраля 2020 г.
- Авторы: Solov’ev Vitaly, Tsivadze Aslan, Marcou Gilles, Varnek Alexandre
- Журнал: Molecular informatics
- Том: 38
- Год издания: 2019
- Издательство: Wiley-VCH Verlag
- Местоположение издательства: Weinheim, Germany, Germany
- Первая страница: 1900002
- DOI: 10.1002/minf.201900002
- Аннотация: Here, we report two-class classification models for organic molecules (“ligands”) able to bind various metal cations in water. The modeling was performed on 30 data sets, each corresponding to a particular metal, using the Naıve Bayes method and the ISIDA fragment descriptors. The ligands were classified on weak and strong binders according to threshold of the logarithm of the stability constant of the 1:1 (metal:ligand) complexes. The “con- sensus models” consisted each of 50 best individual models demonstrated a good predictive performance in 5-fold cross validation: the balanced accuracy (BA) varies from 0.965 (Yb3+ ) to 0.767 (Mg2+ ). The best predictions (BA>0.90) were obtained for the binders of rare-earth metals (Yb3+, Tm3+, Er3+, Lu3+, Ho3+, Gd3+ and Dy3+) and Pb2+. For 17 small external test sets of new ligands, BA varies from 0.800 to 1. The impact of variables selection on the predictive perform- ance of the models is discussed.
- Добавил в систему: Соловьев Виталий Петрович