2017年1月1日
CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning
Molecular Informatics
- ,
- ,
- ,
- ,
- ,
- ,
- 巻
- 36
- 号
- 1
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1002/minf.201600045
- 出版者・発行元
- WILEY-V C H VERLAG GMBH
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.
- リンク情報
-
- DOI
- https://doi.org/10.1002/minf.201600045
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/27515489
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000396406500006&DestApp=WOS_CPL
- Scopus
- https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84981328261&origin=inward 本文へのリンクあり
- Scopus Citedby
- https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=84981328261&origin=inward
- ID情報
-
- DOI : 10.1002/minf.201600045
- ISSN : 1868-1743
- eISSN : 1868-1751
- PubMed ID : 27515489
- SCOPUS ID : 84981328261
- Web of Science ID : WOS:000396406500006