Pharmacophore hypotheses have traditionally been perceived automatically from a small number of manually curated, known active ligands. While chemical diversity of the ligands can vary greatly, generally ten or fewer of them are employed, regardless of how many are available to the project. To expand this number, we have developed a novel algorithm for common pharmacophore perception that uses pharmacophore feature-based shape alignments. Our new PhaseHypoScore function rank-orders hypotheses, generating scores that correlate strongly with enrichment performance in virtual screening. With these methods we can not only perceive common pharmacophores efficiently from hundreds of active ligands, but also rank the hypotheses in a meaningful fashion. In this work we examine the impact of the number and chemical diversity of the active ligands on common pharmacophore perception. In addition, we investigate the ability of the algorithm to recognize multiple binding modes when varying degrees of chemotype diversity are available.

Evaluating use of tens to hundreds of active ligands in pharmacophore hypothesis perception

253rd National Meeting (2017)

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