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Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictionsстатья
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: 18 июля 2013 г.
- Авторы: Zernov V.V., Balakin K.V., Ivaschenko A.A., Savchuk N.P., Pletnev I.V.
- Журнал: Journal of chemical information and computer sciences
- Том: 43
- Номер: 6
- Год издания: 2003
- Издательство: American Chemical Society
- Местоположение издательства: United States
- Первая страница: 2048
- Последняя страница: 2056
- DOI: 10.1021/ci0340916
- Аннотация: Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.
- Добавил в систему: Плетнев Игорь Владимирович