DeepCPI: A Deep Learning-Based Framework for Large-Scale in Silico Drug Screening
Open accessAbstract
Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.
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Prediction of drug-target interactions in small-molecule drug discovery is largely driven by significant improvement in the performance of computational methods.
“This is analogous to small-molecule drug discovery, where computational methods have already made significant inroads [5,6]; prediction of drug-target interactions in particular is largely driven by the significant improvement in the performance of computational methods.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [5]
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- Authors
- Wan, F., Zhu, Y., Hu, H., Dai, A., Cai, X., Chen, L., Gong, H., Xia, T., Yang, D., Wang, M.-W., & Zeng, J.
- Affiliations
- Institute for Interdisciplinary Information Sciences, Tsinghua University , Beijing 100084 , China
- Chinese Academy of Sciences (CN)
- Tsinghua University (CN)
- Huazhong University of Science and Technology (CN)
- Shanghai Medical College of Fudan University (CN)
- Corresponding author
- Yang, Dehua
- Wang, Ming-Wei
- Zeng, Jianyang
- ORCID
- Venue
- Genomics, Proteomics & Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-01
- Volume
- 17
- Issue
- 5
- Pages
- 478-495
- ISSN
- 1672-0229 (ISSN-L)
- 2210-3244
- PMC
- PMC7056933
- OpenAlex
- W3005064662
- Cited by
- 92
- References
- 74
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 20 · 2023: 19 · 2022: 13 · 2021: 12 · 2020: 8
- FWCI
- 5.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genomics Proteomics & Bioinformatics (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61872216, 81630103 to JZ, 81872915 to MWW, 81573479, 81773792 to DY, 81630103, 81872915, 81773792)
- National Science and Technology Major Project (2018ZX09711003-004-002 to LC, 2018ZX09711003-004-002)
- National Science and Technology Major Project Key New Drug Creation and Manufacturing Program of China (2018ZX09735-001 to MWW, 2018ZX09711002-002-005 to DY)
- Shanghai Science and Technology Development Fund (15DZ2291600 to MWW, 16ZR1407100 to AD, 15DZ2291600, 16ZR1407100)
- Key New Drug Creation and Manufacturing Program (2018ZX09735-001, 2018ZX09711002-002-005)
- 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
- compound-protein interactions, in silico drug screening, feature embedding, representation learning, drug-target interactions, G protein-coupled receptors, GLP-1 receptor, glucagon receptor, vasoactive intestinal peptide receptor, drug repurposing, drug discovery
- MeSH terms
- Deep Learning, Pharmaceutical Preparations, Proteins, ROC Curve, User-Computer Interface, Area Under Curve, Databases, Chemical
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