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.
Original language | English (US) |
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Pages (from-to) | 138-146 |
Number of pages | 9 |
Journal | Drug Development Research |
Volume | 72 |
Issue number | 2 |
DOIs | |
State | Published - Mar 2011 |
Keywords
- chemogenomics
- machine learning
- structure-activity-relationship (SAR)