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Journal of Computer-Aided Molecular Design

, Volume 31, Issue 1, pp 1–19 | Cite as

Overview of the SAMPL5 host–guest challenge: Are we doing better?

  • Jian Yin
  • Niel M. Henriksen
  • David R. Slochower
  • Michael R. Shirts
  • Michael W. Chiu
  • David L. Mobley
  • Michael K. Gilson
Article

Abstract

The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein–ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host–guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host–guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host–guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.

Keywords

Host–guest Molecular recognition Computer-aided drug design Blind challenge Binding affinity 

Notes

Acknowledgments

We thank Dr. Pär Söderhjelm for helpful discussion on the error analysis and Dr. Ulf Ryde for helpful comments on the manuscript. M.K.G. thanks the National Institutes of Health (NIH) for Grants GM061300 and U01GM111528 and the Air Force Office of Scientific Research (AFOSR) for Basic Research Initiative (BRI) Grant (FA9550-12-1-6440414). D.L.M appreciates financial support from the National institutes of Health (1R01GM108889-01) and the National Science Foundation (CHE 1352608). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the NIH, NSF or AFOSR. M.K.G. has an equity interest in, and is a cofounder and scientific advisor of VeraChem LLC.

Supplementary material

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jian Yin
    • 1
  • Niel M. Henriksen
    • 1
  • David R. Slochower
    • 1
  • Michael R. Shirts
    • 2
  • Michael W. Chiu
    • 3
  • David L. Mobley
    • 4
  • Michael K. Gilson
    • 1
  1. 1.Skaggs School of Pharmacy and Pharmaceutical SciencesUniversity of California San DiegoLa JollaUSA
  2. 2.Department of Chemical and Biological EngineeringUniversity of Colorado BoulderBoulderUSA
  3. 3.Qualcomm InstituteUniversity of California, San DiegoLa JollaUSA
  4. 4.Departments of Pharmaceutical Sciences and ChemistryUniversity of California IrvineIrvineUSA

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