Pharmacophore models are widely used for the identification of promising primary hits in compound large libraries. Recent studies have demonstrated that pharmacophores retrieved from protein-ligand molecular dynamic trajectories outperform pharmacophores retrieved from a single crystal complex structure. However, the number of retrieved pharmacophores can be enormous, thus, making it computationally inefficient to use all of them for virtual screening. In this study, we proposed selection of distinct representative pharmacophores by the removal of pharmacophores with identical three-dimensional (3D) pharmacophore hashes. We also proposed a new conformer coverage approach in order to rank compounds using all representative pharmacophores. Our results for four cyclin-dependent kinase 2 (CDK2) complexes with different ligands demonstrated that the proposed selection and ranking approaches outperformed the previously described common hits approach. We also demonstrated that ranking, based on averaged predicted scores obtained from different complexes, can outperform ranking based on scores from an individual complex. All developments were implemented in open-source software pharmd.

A M Butlerov Institute of Chemistry Kazan Federal University Kremlyovskaya Str 18 420008 Kazan Russia

Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacky University and University Hospital in Olomouc Hnevotinska 5 77900 Olomouc Czech Republic

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Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores