Structure-based virtual ligand screening with LigandFit: pose prediction and enrichment of compound collections

Virtual ligand screening methods based on the structure of the receptor are extensively used to facilitate the discovery of lead compounds. In the present study, we investigated the LigandFit package on four different proteins (coagulation factor VIIa, estrogen receptor, thymidine kinase, and neuraminidase), a relatively large compound collection of 65,560 unique "drug-like" molecules and four focused libraries (1950 molecules each). We performed virtual screening experiments with the large database and evaluated six scoring functions available in the package (DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF). The results showed that LigandFit is an efficient program, especially when used with LigScore1. Similar computations were carried out using focused libraries. In this situation the LigScore1 scoring function outperformed the other ones on three out of the four proteins tested. Even for the difficult neuraminidase case, the LigandFit/LigScore1 combination was still reasonably successful. Assessment of docking accuracy was also performed and again, we found that LigandFit (with DockScore and the CFF parameters) was performing well. On the basis of these results and observed increased enrichments after LigandFit/Ligscore1 screening on focused libraries, we suggest that using this program as a final step of a hierarchical protocol can be very beneficial to assist lead finding.