DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening

Publication date: Available online 6 February 2020Source: Genomics, Proteomics & BioinformaticsAuthor(s): Fangping Wan, Yue Zhu, Hailin Hu, Antao Dai, Xiaoqing Cai, Ligong Chen, Haipeng Gong, Tian Xia, Dehua Yang, Mingwei Wang, Jianyang ZengAbstractAccurate 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 proposed 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...

Source: Genomics, Proteomics and Bioinformatics - February 7, 2020 Category: Bioinformatics Source Type: research