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In silico structure‐activity‐relationship (SAR) models from machine learning: a review
Author(s) -
Ning Xia,
Karypis George
Publication year - 2011
Publication title -
drug development research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.582
H-Index - 60
eISSN - 1098-2299
pISSN - 0272-4391
DOI - 10.1002/ddr.20410
Subject(s) - in silico , computer science , machine learning , artificial intelligence , biology , biochemistry , gene
In this article, we review the recent development for in silico Structure‐Activity‐Relationship (SAR) models using machine‐learning techniques. The review focuses on the following topics: machine‐learning algorithms for computational SAR models, single‐target‐oriented SAR methodologies, Chemogenomics, and future trends. We try to provide the state‐of‐the‐art SAR methods as well as the most up‐to‐date advancement, in order for the researchers to have a general overview at this area. Drug Dev Res 72: 138–146, 2011. © 2010 Wiley‐Liss, Inc.