Enhancing Specificity and Sensitivity of Pharmacophore-Based Virtual Screening by Incorporating Chemical and Shape Features−A Case Study of HIV Protease Inhibitors

Virtual screening (VS), if applied appropriately, could significantly shorten the hit identification and hit-to-lead processes in drug discovery. Recently, the version of VS that is based upon similarity to a
pharmacophore has received increased attention. This is due to two major factors:  first, the public availability
of the ZINC conformational database has provided a large selection pool with high-quality and purchasable
small molecules; second, new technology has enabled a more accurate and flexible definition of
pharmacophore models coupled with an efficient search speed. The major goal of this study was to achieve
improved specificity and sensitivity of pharmacophore-based VS by optimizing the variables used to generate
conformations of small molecules and those used to construct pharmacophore models from known inhibitors
or from inhibitor−protein complex structures. By using human immunodeficiency virus protease and its
inhibitors (PIs) as a case study, the impact of the key variables, including the selection of chemical features,
involvement of excluded volumes (EV), the tolerance radius of excluded volumes, energy windows, and
the maximum number of conformers in conformation generation, was explored. Protein flexibility was
simulated by adjusting the sizes of EV. Our best pharmacophore model, combining both chemical features
and excluded volumes, was able to correctly identify 60 out of 75 structurally diverse known PIs, while
misclassifying only 5 out of 75 similar compounds that are not inhibitors. To evaluate the specificity of the
model, 1193 oral drugs on the market were screened, and 25 original hits were identified, including 5 out
of 6 known PI drugs.