Journal
EXPERT OPINION ON DRUG METABOLISM & TOXICOLOGY
Volume 10, Issue 9, Pages 1273-1287Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1517/17425255.2014.950222
Keywords
chemogenomic; drug-target; drug-target interaction; machine learning
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Introduction: Identification of the interaction between drugs and target proteins is a crucial task in genomic drug discovery. The in silico prediction is an appropriate alternative for the laborious and costly experimental process of drug-target interaction prediction. Developing a variety of computational methods opens a new direction in analyzing and detecting new drug-target pairs. Areas covered: In this review, we will focus on chemogenomic methods which have established a learning framework for predicting drug-target interactions. Learning-based methods are classified into supervised and semi-supervised, and the supervised learning methods are studied as two separate parts including similarity-based methods and feature-based methods. Expert opinion: In spite of many improvements for pharmacology applications by learning-based methods, there are many over simplification settings in construction of predictive models that may lead to over-optimistic results on drug-target interaction prediction.
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