SAMPL4 & DOCK3.7: lessons for automated docking procedures

Abstract

The SAMPL4 challenges were used to test current automated methods for solvation energy, virtual screening, pose and affinity prediction of the molecular docking pipeline DOCK 3.7. Additionally, first-order models of binding affinity were proposed as milestones for any method predicting binding affinity. Several important discoveries about the molecular docking software were made during the challenge: (1) Solvation energies of ligands were five-fold worse than any other method used in SAMPL4, including methods that were similarly fast, (2) HIV Integrase is a challenging target, but automated docking on the correct allosteric site performed well in terms of virtual screening and pose prediction (compared to other methods) but affinity prediction, as expected, was very poor, (3) Molecular docking grid sizes can be very important, serious errors were discovered with default settings that have been adjusted for all future work. Overall, lessons from SAMPL4 suggest many changes to molecular docking tools, not just DOCK 3.7, that could improve the state of the art. Future difficulties and projects will be discussed.

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Acknowledgments

We acknowledge financial support from National Research Service Award-Kirschstein fellowships F32GM096544 (to RGC) and F32GM093580 (to DRW), and US National Institutes of Health grants GM59957 and GM71630 (to Brian K. Shoichet). We would also like to thank the SAMPL4 organizers, as well as ChemAxon for Marvin & cxcalc, OpenEye for OMEGA and OEtools. Final acknowledgements to Paul Hawkins for motivation and guidance on statistical methods.

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Correspondence to Ryan G. Coleman.

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Coleman, R.G., Sterling, T. & Weiss, D.R. SAMPL4 & DOCK3.7: lessons for automated docking procedures. J Comput Aided Mol Des 28, 201–209 (2014). https://doi.org/10.1007/s10822-014-9722-6

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Keywords

  • Molecular docking
  • Solvation
  • SAMPL
  • First-order models