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Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges

Abstract

The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ ~ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ ~ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.

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Acknowledgements

We thank OpenEye Scientific for providing an academic license for their software. The work was funded by U.S. National Institutes of Health Grant Numbers GM097082 and T32EB009403.

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Correspondence to Carlos J. Camacho.

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Wingert, B.M., Oerlemans, R. & Camacho, C.J. Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges. J Comput Aided Mol Des 32, 287–297 (2018). https://doi.org/10.1007/s10822-017-0065-y

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  • DOI: https://doi.org/10.1007/s10822-017-0065-y

Keywords

  • D3R
  • Drug Design Data Resource
  • Virtual screening
  • Affinity ranking
  • Pose prediction