Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

• Published in: Briefings in bioinformatics Vol. 22; no. 1; pp. 247 - 269
• Main Authors: Bagherian, Maryam, Sabeti, Elyas, Wang, Kai, Sartor, Maureen A, Nikolovska-Coleska, Zaneta, Najarian, Kayvan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 18.01.2021; Oxford Publishing Limited (England)
• Subjects: Computational Biology - methods; Databases, Factual; Drug Discovery - methods; Humans; Learning algorithms; Machine Learning; Predictions; Review; Therapeutic targets; DTI database; DTI software; drug–target interaction prediction; Machine learning
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbz157

Abstract The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.