A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators.

We developed a pharmacophore-based evolutionary approach for virtual screening. This tool, termed the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach with a new pharmacophore-based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to expedite convergence. The latter, integrating an empirical-based energy function and pharmacological preferences (binding-site pharmacological interactions and ligand preferences), simultaneously serves as the scoring function for both molecular docking and postdocking analyses to improve screening accuracy. We apply pharmacological interaction preferences to select the ligands that form pharmacological interactions with target proteins, and use the ligand preferences to eliminate the ligands that violate the electrostatic or hydrophilic constraints. We assessed the accuracy of our approach using human estrogen receptor (ER) and a ligand database from the comparative studies of Bissantz et al. (J Med Chem 2000;43:4759-4767). Using GEMDOCK, the average goodness-of-hit (GH) score was 0.83 and the average false-positive rate was 0.13% for ER antagonists, and the average GH score was 0.48 and the average false-positive rate was 0.75% for ER agonists. The performance of GEMDOCK was superior to competing methods such as GOLD and DOCK. We found that our pharmacophore-based scoring function indeed was able to reduce the number of false positives; moreover, the resulting pharmacological interactions at the binding site, as well as ligand preferences, were important to the screening accuracy of our experiments. These results suggest that GEMDOCK constitutes a robust tool for virtual database screening.