期刊
JOURNAL OF MOLECULAR MODELING
卷 24, 期 10, 页码 -出版社
SPRINGER
DOI: 10.1007/s00894-018-3820-7
关键词
Ensemble; Structure-based pharmacophore; Probabilistic model; Protein-ligand complex; Virtual screening; Entropy
类别
资金
- Science and Engineering Research Board (SERB), Government of India [PDF/2016/000156]
Ensemble methods are gaining more importance in structure-based approaches as single protein-ligand complexes strongly influence the outcomes of virtual screening. Structure-based pharmacophore modeling based on a single protein-ligand complex with complex feature combinations is often limited to certain chemical classes. The REPHARMBLE (receptor pharmacophore ensemble) approach presented here examines the ability of an ensemble of selected protein-ligand complexes to populate pharmacophore space in the ligand binding site, rigorously assesses the importance of pharmacophore features using Poisson statistic and information theory-based entropy calculations, and generates pharmacophore models with high probabilities. In addition, an ensemble scoring function that combines all the resultant high-scoring pharmacophore models to score molecules is derived. The REPHARMBLE approach was evaluated on ten DUD-E benchmark datasets and afforded good screening performance, as measured by receiver operating characteristic, enrichment factor and Guner-Henry score. Although one of the high-scoring models achieved superior statistical results in each dataset, the ensemble scoring function balanced the shortcomings of each model and passed with close performance measures. This approach offers a reliable way of choosing the best-scoring features to build four-feature pharmacophore queries and customize a target-biased pharmacophore ensemble' scoring function for subsequent virtual screening.
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