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

Xia Ning, George Karypis

Research output: Contribution to journalReview articlepeer-review

8 Scopus citations

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 languageEnglish (US)
Pages (from-to)138-146
Number of pages9
JournalDrug Development Research
Volume72
Issue number2
DOIs
StatePublished - Mar 2011

Keywords

  • chemogenomics
  • machine learning
  • structure-activity-relationship (SAR)

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