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

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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Briefings in bioinformatics Ročník 22; číslo 1; s. 247 - 269
Hlavní autori: Bagherian, Maryam, Sabeti, Elyas, Wang, Kai, Sartor, Maureen A, Nikolovska-Coleska, Zaneta, Najarian, Kayvan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Oxford University Press 18.01.2021
Oxford Publishing Limited (England)
Predmet:
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, 1477-4054
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbz157