CGBVS‐DNN: Prediction of Compound‐protein Interactions Based on Deep Learning. Issue 1 (12th August 2016)

• Record Type: Journal Article
• Authors: Hamanaka, Masatoshi; Taneishi, Kei; Iwata, Hiroaki; Ye, Jun; Pei, Jianguo; Hou, Jinlong; Okuno, Yasushi
• Other Names: Schneider Gisbert guestEditor.; Funatsu Kimito guestEditor.; Okuno Ysushi guestEditor.; Winkler Dave guestEditor.
• Is Part Of: Molecular informatics. Volume 36:Issue 1/2(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-08-12
• Subjects: deep learning -- in-silico screening -- compound-protein interactions (cpis) -- chemical genomics-based virtual screening (cgbvs) -- support vector machine; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201600045
• Languages: English
• ISSNs: 1868-1743

Abstract

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. Abstract :