標題: GemAffinity: a scoring function for predicting binding affinity and Virtual Screening
作者: Hsu, Kai-Cheng
Chen, Yen-Fu
Yang, Jinn-Moon
生物科技學系
生物資訊及系統生物研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
關鍵字: binding affinity prediction;scoring functions;structure-based drug design;metal-ligand interactions;water effects;data mining;bioinformatics
公開日期: 2012
摘要: 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.
URI: http://hdl.handle.net/11536/15701
http://dx.doi.org/10.1504/IJDMB.2012.045535
ISSN: 1748-5673
DOI: 10.1504/IJDMB.2012.045535
期刊: INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS
Volume: 6
Issue: 1
起始頁: 27
結束頁: 41
Appears in Collections:Articles