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Adv Drug Deliv Rev. 2015 Jun 23;86:83-100. doi: 10.1016/j.addr.2015.03.014. Epub 2015 May 30.

Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools.

Author information

1
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China; Bioinformatics and Drug Design Group, Department of Pharmacy, Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.
2
Bioinformatics and Drug Design Group, Department of Pharmacy, Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore.
3
Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, No. 54 Youdian Road, Hangzhou 310006, China.
4
Innovative Drug Research Centre and College of Chemistry and Chemical Engineering, Chongqing University, Chongqing, China.
5
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China.
6
Bioinformatics and Drug Design Group, Department of Pharmacy, Center for Computational Science and Engineering, National University of Singapore, Singapore 117543, Singapore. Electronic address: phacyz@nus.edu.sg.

Abstract

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.

KEYWORDS:

ADME; Absorption; Distribution; Drug discovery; Excretion; Machine learning; Metabolism; Molecular descriptors; QSAR

PMID:
26037068
DOI:
10.1016/j.addr.2015.03.014
[Indexed for MEDLINE]

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