Cited by 294Computational Drug Discovery MethodsMachine Learning in BioinformaticsBioinformatics and Genomic Networks

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Machine Learning for Drug-Target Interaction Prediction

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Chen, Ruolan, Liu, Xiangrong, Jin, Shuting, Lin, Jiawei, Liu, Juan·2018·Molecules

DOI 10.3390/molecules23092208PMC PMC6225477

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Abstract

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.

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Authors: Chen, R., Liu, X., Jin, S., Lin, J., & Liu, J.
Affiliations: Xiamen University (CN)
Corresponding author: Liu, Juan
ORCID: Chen, Ruolan
Liu, Xiangrong
Jin, Shuting
Lin, Jiawei
Liu, Juan
Venue: Molecules
Publisher: MDPI AG
Published: 2018-08-31
Volume: 23
Issue: 9
Pages: 2208
ISSN: 1420-3049 (ISSN-L)
DOI: 10.3390/molecules23092208
PMC: PMC6225477
OpenAlex: W2889321024
Cited by: 292
References: 74
Citations by year: 2026: 22 · 2025: 34 · 2024: 56 · 2023: 44 · 2022: 49 · 2021: 41 · 2020: 24 · 2019: 22
FWCI: 1.70
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Molecules (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, other-oa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Natural Science Foundation of China (61472333, 61772441, 61472335)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: drug-target interaction prediction, machine learning, drug discovery, hierarchical classification, computational drug discovery
MeSH terms: Machine Learning, Algorithms, Drug Interactions, Humans, Drug Delivery Systems, Databases as Topic, Drug Discovery

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Machine Learning for Drug-Target Interaction Prediction

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