Title: GemAffinity: a scoring function for predicting binding affinity and Virtual Screening

Authors: Kai-Cheng Hsu; Yen-Fu Chen; Jinn-Moon Yang

Addresses: Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan. ' Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan. ' Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 30050, Taiwan; Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 30050, Taiwan; Core Facility for Structural Bioinformatics, National Chiao Tung University, Hsinchu, Taiwan

Abstract: Prediction of protein-ligand binding affinities plays an essential role for molecular recognition and virtual screening. We have developed a scoring function, namely GemAffinity, to predict binding affinities by using a stepwise regression method and 88 descriptors from 891 complex structures. GemAffinity consists of five descriptors, including van der Waals contacts; metal-ligand interactions; water effects; ligand deformation penalty; and conserved hydrogen-bonded residues. Experimental results indicate that GemAffinity is the best among 13 methods on a test set and can enrich screening accuracies on four sets. We believe that GemAffinity is useful for virtual screening and drug discovery.

Keywords: binding affinity prediction; scoring functions; structure-based drug design; metal–ligand interactions; water effects; data mining; bioinformatics; virtual screening; protein-ligand binding affinities; molecular recognition; ligand deformation; hydrogen-bonded residues; drug discovery.

DOI: 10.1504/IJDMB.2012.045535

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.1, pp.27 - 41

Received: 10 Feb 2010
Accepted: 20 Feb 2010
Published online: 17 Dec 2014 *

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