Fuzzy pharmacophore models from molecular alignments for correlation-vector-based virtual screening.

A pharmacophore-based approach for compiling focused screening libraries is presented. It integrates information from three-dimensional molecular alignments into correlation vector-based database screening. The pharmacophore model is represented by a number of spheres of Gaussian-distributed feature densities. Different degrees of "fuzziness" can be introduced to influence the model's resolution. Transformation of this pharmacophore representation into a correlation vector results in a vector of feature probabilities which can be utilized for rapid virtual screening of compound databases or virtual libraries. The approach was validated by retrospective screening for cyclooxygenase 2 (COX-2) and thrombin ligands. A variety of models with different degrees of fuzziness were calculated and tested for both classes of molecules. Best performance was obtained with pharmacophore models reflecting an intermediate degree of fuzziness, yielding an enrichment factor of up to 39 for the first 1% of the ranked database. Appropriately weighted fuzzy pharmacophore models performed better in retrospective screening than similarity searching using only a single query molecule. The new pharmacophore method was shown to complement existing approaches.