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 |
卷 | 6 |
發行號 | 1 |
DOIs | |
出版狀態 | 已發佈 - 2月 2012 |
對外發佈 | 是 |
ASJC Scopus subject areas
- 資訊系統
- 一般生物化學,遺傳學和分子生物學
- 圖書館與資訊科學