title: Drug-target interaction prediction via chemogenomic space: learning-based methods.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: University of Tehran, Institute of Biochemistry and Biophysics, Laboratory of Systems Biology and Bioinformatics (LBB) , Tehran , Iran +98 21 6695 9256 ; +98 21 6640 4680 ; amasoudin@ibb.ut.ac.ir.
authors.forename: Zaynab
authors.initials: Z
authors.lastname: Mousavian
authors.forename: Ali
authors.initials: A
authors.lastname: Masoudi-Nejad
mesh_terms: D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D058990:Molecular Targeted Therapy
keywords: chemogenomic; drug-target; drug-target interaction; machine learning