title: Boosting compound-protein interaction prediction by deep learning.
abstract: The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets.
authors.affiliation: Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China. Electronic address: ktian14@fudan.edu.cn.
authors.forename: Kai
authors.initials: K
authors.lastname: Tian
authors.affiliation: Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China. Electronic address: shaomy@fudan.edu.cn.
authors.forename: Mingyu
authors.initials: M
authors.lastname: Shao
authors.affiliation: School of Software, Jiangxi Normal University, Nanchang 330022, China. Electronic address: yang1995t@163.com.
authors.forename: Yang
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Technology, Tongji University, Shanghai 201804, China. Electronic address: jhguan@tongji.edu.cn.
authors.forename: Jihong
authors.initials: J
authors.lastname: Guan
authors.affiliation: Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China. Electronic address: sgzhou@fudan.edu.cn.
authors.forename: Shuigeng
authors.initials: S
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D062126:Databases, Chemical; D030562:Databases, Protein; D004339:Drug Compounding; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry; D000072417:Protein Domains / Q000187:drug effects; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins
keywords: Compound-protein interaction; Deep learning; Deep neural network (DNN)