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In silico structure-activity-relationship (SAR) models from machine learning: a review

✍ Scribed by Xia Ning; George Karypis

Publisher: John Wiley and Sons
Year: 2010
Tongue: English
Weight: 98 KB
Volume: 72
Category: Article
ISSN: 0272-4391
DOI: 10.1002/ddr.20410

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✦ Synopsis

Abstract

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.

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