A pharmacophore-based evolutionary approach for screening estrogen receptor antagonists

Virtual ligand screening has emerged as a promising approach for drug development. The inaccuracy of the scoring methods is probably major weakness for virtual ligand screening. In this paper, we have developed a pharmacophore-based evolutionary approach that was applicable to virtual screening and postdocking analysis. Our tool, referred to as the genetic evolutionary method for molecular docking (GEMDOCK), combines an evolutionary approach and a pharmacophore-based scoring function for virtual database screening. The former integrates discrete and continuous global search strategies with local search strategies to speed up convergence. The latter integrates a simple empirical scoring function and pharmacophore preferences. We accessed the screening accuracy of our approach on estrogen receptor alpha (ERα) using a ligand database on which competing tools were evaluated. The accuracies of our prediction were 0.64 for the GH score and 0.91% for the false positive rate when the true positive rate was 100%. We found that our pharmacophore-based scoring function indeed is able to reduce the number of the false positives. These results suggest that GEMDOCK is robust and can be a useful tool for virtual database screening.