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Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
- Source :
- Briefings in Bioinformatics
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 2020.
-
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.
- Subjects :
- Databases, Factual
AcademicSubjects/SCI01060
Computer science
Process (engineering)
Drug target
Review Article
computer.software_genre
Machine learning
Task (project management)
Machine Learning
03 medical and health sciences
0302 clinical medicine
Drug Discovery
DTI software
Humans
Set (psychology)
Molecular Biology
030304 developmental biology
0303 health sciences
Database
business.industry
drug–target interaction prediction
Computational Biology
Key (cryptography)
Artificial intelligence
Erratum
business
computer
DTI database
030217 neurology & neurosurgery
Information Systems
Subjects
Details
- ISSN :
- 14774054 and 14675463
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- Briefings in Bioinformatics
- Accession number :
- edsair.doi.dedup.....a407beb8c5295bd06b23e960a95702e2