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 language | English |
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Pages (from-to) | 27-41 |
Number of pages | 15 |
Journal | International Journal of Data Mining and Bioinformatics |
Volume | 6 |
Issue number | 1 |
DOIs | |
Publication status | Published - Feb 2012 |
Externally published | Yes |
Keywords
- Binding affinity prediction
- Bioinformatics
- Data mining
- Metal-ligand interactions
- Scoring functions
- Structure-based drug design
- Water effects
ASJC Scopus subject areas
- Information Systems
- General Biochemistry,Genetics and Molecular Biology
- Library and Information Sciences