Drug–target interaction prediction via chemogenomic space: learning-based methods

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.