Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.
Bayes Theorem
Cheminformatics
Computational Biology
Computer Simulation
Decision Trees
Drug Development
/ methods
Drug Discovery
/ methods
Drug Interactions
Drug Repositioning
/ methods
Fuzzy Logic
Humans
Least-Squares Analysis
Machine Learning
Models, Statistical
Pharmacogenomic Testing
/ methods
Support Vector Machine
Surveys and Questionnaires
drug-target interaction prediction
machine learning
Journal
Briefings in bioinformatics
ISSN: 1477-4054
Titre abrégé: Brief Bioinform
Pays: England
ID NLM: 100912837
Informations de publication
Date de publication:
19 07 2019
19 07 2019
Historique:
received:
06
10
2017
revised:
21
12
2017
pubmed:
30
1
2018
medline:
10
4
2020
entrez:
30
1
2018
Statut:
ppublish
Résumé
Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.
Identifiants
pubmed: 29377981
pii: 4824712
doi: 10.1093/bib/bby002
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1337-1357Informations de copyright
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.