2010年11月
Prediction of protein-ligand binding affinities using multiple instance learning
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
- ,
- 巻
- 29
- 号
- 3
- 開始ページ
- 492
- 終了ページ
- 497
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.jmgm.2010.09.006
- 出版者・発行元
- ELSEVIER SCIENCE INC
Accurate prediction of protein-ligand binding affinities for lead optimization in drug discovery remains an important and challenging problem on scoring functions for docking simulation. In this paper, we propose a data-driven approach that integrates multiple scoring functions to predict protein-ligand binding affinity directly. We then propose a new method called multiple instance regression based scoring (MIRS) that incorporates unbound ligand conformations using multiple scoring functions. We evaluated the predictive performance of MIRS using 100 protein-ligand complexes and their binding affinities. The experimental results showed that MIRS outperformed the 11 conventional scoring functions including LigScore, PLP, AutoDock, G-Score, D-Score, LUDI, F-Score, ChemScore, X-Score, PMF, and DrugScore. In addition, we confirmed that MIRS performed well on binding pose prediction. Our results reveal that it is indispensable to incorporate unbound ligand conformations in both binding affinity prediction and binding pose prediction. The proposed method will accelerate efficient lead optimization on structure-based drug design and provide a new direction to designing of new scoring score functions. (C) 2010 Elsevier Inc. All rights reserved.
- リンク情報
-
- DOI
- https://doi.org/10.1016/j.jmgm.2010.09.006
- J-GLOBAL
- https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201002216526019895
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/20965757
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000285402500021&DestApp=WOS_CPL
- ID情報
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- DOI : 10.1016/j.jmgm.2010.09.006
- ISSN : 1093-3263
- J-Global ID : 201002216526019895
- PubMed ID : 20965757
- Web of Science ID : WOS:000285402500021