Enhancing Success Rates in Virtual Screening

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July 21, 2011

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  • Virtual screening (VS) has emerged as a straightforward computational tool for the rapid identification of bioactive compounds from large collections of chemicals. Typically, VS is performed with molecular docking, which is able to virtually screen and rank a large number of molecules into the active site of a target structure in a reasonable amount of time. This technique is powerful and cost-effective, allowing prioritisation and selection of best-hit compounds for biological assays based on the strength of interaction with the biological target. However, the inherent limitations in the accuracy of these methods often lead to many false positives or false negatives in the hit lists. To this end, we developed Binding Estimation After Refinement (BEAR), a new automated post-docking procedure for the conformational refinement of docking poses through molecular dynamics followed by accurate prediction of binding free energies using MM-PBSA and MM-GBSA. BEAR was validated on several macromolecular targets and known ligands, by determining the enrichment factors and assessing the correlation between predicted and experimental binding affinities, and then applied in real drug discovery campaigns. The data obtained suggested critical improvements with respect to standard docking methods and impressive hit rates when applied in drug discovery.

    Drug Discovery

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