Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest

Bibliographische Detailangaben
Veröffentlicht in:	Journal of computational chemistry. - 1984. - 38(2017), 3 vom: 30. Jan., Seite 169-177
1. Verfasser:	Wang, Cheng (VerfasserIn)
Weitere Verfasser:	Zhang, Yingkai
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2017
Zugriff auf das übergeordnete Werk:	Journal of computational chemistry
Schlagworte:	Journal Article Research Support, Non-U.S. Gov't docking machine learning protein-ligand binding affinity random forest scoring function Ligands Proteins

Beschreibung
Zusammenfassung:	© 2016 Wiley Periodicals, Inc. The development of new protein-ligand scoring functions using machine learning algorithms, such as random forest, has been of significant interest. By efficiently utilizing expanded feature sets and a large set of experimental data, random forest based scoring functions (RFbScore) can achieve better correlations to experimental protein-ligand binding data with known crystal structures; however, more extensive tests indicate that such enhancement in scoring power comes with significant under-performance in docking and screening power tests compared to traditional scoring functions. In this work, to improve scoring-docking-screening powers of protein-ligand docking functions simultaneously, we have introduced a Δvina RF parameterization and feature selection framework based on random forest. Our developed scoring function Δvina RF20 , which employs 20 descriptors in addition to the AutoDock Vina score, can achieve superior performance in all power tests of both CASF-2013 and CASF-2007 benchmarks compared to classical scoring functions. The Δvina RF20 scoring function and its code are freely available on the web at: https://www.nyu.edu/projects/yzhang/DeltaVina. © 2016 Wiley Periodicals, Inc
Beschreibung:	Date Completed 22.08.2017 Date Revised 03.06.2024 published: Print-Electronic Citation Status MEDLINE
ISSN:	1096-987X
DOI:	10.1002/jcc.24667