title: CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning.
abstract: Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a support vector machine (SVM). However, the CGBVS has problems when training using more than a million datasets of CPIs since SVMs require an exponential increase in the calculation time and computer memory. To solve this problem, we propose the CGBVS-DNN, in which we use deep neural networks, a kind of deep learning technique, instead of the SVM. Deep learning does not require learning all input data at once because the network can be trained with small mini-batches. Experimental results show that the CGBVS-DNN outperformed the original CGBVS with a quarter million CPIs. Results of cross-validation show that the accuracy of the CGBVS-DNN reaches up to 98.2 % (σ<0.01) with 4 million CPIs.
authors: Graduate School of Medicine, Kyoto University, Shogoin-kawaharacho, city/>Sakyo-ku Kyoto, 606-8507, Japan.
authors: Masatoshi
authors: M
authors: Hamanaka
authors: Advanced Institute for Computational Science, RIKEN, 7-1-28, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.
authors: Kei
authors: K
authors: Taneishi
authors: Foundation for Biomedical Research and Innovation, 1-6-5, Minatojima-Minamimachi Chuo-ku, Kobe, 650-0047, Japan.
authors: Hiroaki
authors: H
authors: Iwata
authors: Software and Services Group, Intel Corporation, Shanghai, China.
authors: Jun
authors: J
authors: Ye
authors: Software and Services Group, Intel Corporation, Shanghai, China.
authors: Jianguo
authors: J
authors: Pei
authors: Software and Services Group, Intel Corporation, Shanghai, China.
authors: Jinlong
authors: J
authors: Hou
authors: Graduate School of Medicine, Kyoto University, Shogoin-kawaharacho, city/>Sakyo-ku Kyoto, 606-8507, Japan.
authors: Yasushi
authors: Y
authors: Okuno
mesh_terms: D001665:Binding Sites; D000069550:Machine Learning; D062105:Molecular Docking Simulation / Q000379:methods* / Q000592:standards; D011485:Protein Binding; D020543:Proteome / Q000737:chemistry / Q000378:metabolism; D012984:Software
chemical_list: D020543:Proteome
keywords: chemical genomics-based virtual screening (cgbvs); compound-protein interactions (cpis); deep learning; in-silico screening; support vector machine