Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

Abstract While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discove...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 20; no. 4; pp. 1465 - 1474
Main Authors: Hao, Ming, Bryant, Stephen H, Wang, Yanli
Format: Journal Article
Language:English
Published: England Oxford University Press 19.07.2019
Oxford Publishing Limited (England)
Subjects:
Accessibility
Algorithms
Availability
Computational Biology
Computer applications
Computer Simulation
Drug development
Drug Development - methods
Drug Development - statistics & numerical data
Drug discovery
Drug Discovery - methods
Drug Discovery - statistics & numerical data
Drug Repositioning - methods
Drug Repositioning - statistics & numerical data
Gene sequencing
Genomes
High-throughput screening
Humans
Pharmaceutical industry
Pharmacogenomic Testing - methods
Pharmacogenomic Testing - statistics & numerical data
Programming languages
R&D
Research & development
Therapeutic targets
Whole genome sequencing
drug–target interaction
drug discovery
mean percentile ranking
chemogenomic data
open-source code
in silico drug repositioning
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bby010