期刊
ADVANCED DRUG DELIVERY REVIEWS
卷 86, 期 -, 页码 83-100出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.addr.2015.03.014
关键词
ADME; Absorption; Distribution; Metabolism; Excretion; Drug discovery; Machine learning; Molecular descriptors; QSAR
资金
- Major State Basic Research Development Program of China [2013CB967204]
- Singapore Academic Research Fund [R148000181112]
In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties. (C) 2015 Published by Elsevier B.V.
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