GemAffinity: A scoring function for predicting binding affinity and Virtual Screening

Kai Cheng Hsu, Yen Fu Chen, Jinn Moon Yang

研究成果: 雜誌貢獻文章

3 引文 斯高帕斯(Scopus)

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.
原文英語
頁(從 - 到)27-41
頁數15
期刊International Journal of Data Mining and Bioinformatics
6
發行號1
DOIs
出版狀態已發佈 - 二月 2012
對外發佈Yes

ASJC Scopus subject areas

  • Library and Information Sciences
  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)

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