GemAffinity: a scoring function for predicting binding affinity and Virtual Screening
by Kai-Cheng Hsu; Yen-Fu Chen; Jinn-Moon Yang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 6, No. 1, 2012

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.

Online publication date: Wed, 17-Dec-2014

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