DeepCPI:A Deep Learning-based Framework for Large-scale in silico Drug Screening |
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Affiliation: | Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China;The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China;School of Medicine, Tsinghua University, Beijing 100084, China;School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China;School of Life Science, Tsinghua University, Beijing 100084, China;Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;The National Center for Drug Screening and the CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China;School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China;Shanghai Medical College, Fudan University, Shanghai 200032, China;Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China;MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China |
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Abstract: | 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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Keywords: | Deep learning Machine learning Drug discovery Compound–protein interaction prediction |
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