Machine Learning for Drug-Target Interaction Prediction
Open accessAbstract
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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AI algorithms including supervised and unsupervised learning, reinforcement, and evolutionary or rule-based algorithms can potentially contribute to solving problems of current medicinal chemistry methods.
“Different algorithms based on AI, including supervised and unsupervised learning methods, reinforcement, and evolutionary or rule-based algorithms, can potentially contribute to solving these problems.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [9]
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- Authors
- Chen, R., Liu, X., Jin, S., Lin, J., & Liu, J.
- Affiliations
- Xiamen University (CN)
- Corresponding author
- Liu, Juan
- Venue
- Molecules
- Publisher
- MDPI AG
- Published
- 2018-08-31
- Volume
- 23
- Issue
- 9
- Pages
- 2208
- ISSN
- 1420-3049 (ISSN-L)
- 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
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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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