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

Kai Cheng Hsu, Yen Fu Chen, Jinn Moon Yang

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)27-41
Number of pages15
JournalInternational Journal of Data Mining and Bioinformatics
Volume6
Issue number1
DOIs
Publication statusPublished - Feb 2012
Externally publishedYes

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Keywords

  • Binding affinity prediction
  • Bioinformatics
  • Data mining
  • Metal-ligand interactions
  • Scoring functions
  • Structure-based drug design
  • Water effects

ASJC Scopus subject areas

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

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