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Blind prediction of solvation free energies from the SAMPL4 challenge

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Abstract

Here, we give an overview of the small molecule hydration portion of the SAMPL4 challenge, which focused on predicting hydration free energies for a series of 47 small molecules. These gas-to-water transfer free energies have in the past proven a valuable test of a variety of computational methods and force fields. Here, in contrast to some previous SAMPL challenges, we find a relatively wide range of methods perform quite well on this test set, with RMS errors in the 1.2 kcal/mol range for several of the best performing methods. Top-performers included a quantum mechanical approach with continuum solvent models and functional group corrections, alchemical molecular dynamics simulations with a classical all-atom force field, and a single-conformation Poisson–Boltzmann approach. While 1.2 kcal/mol is still a significant error, experimental hydration free energies covered a range of nearly 20 kcal/mol, so methods typically showed substantial predictive power. Here, a substantial new focus was on evaluation of error estimates, as predicting when a computational prediction is reliable versus unreliable has considerable practical value. We found, however, that in many cases errors are substantially underestimated, and that typically little effort has been invested in estimating likely error. We believe this is an important area for further research.

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Notes

  1. For experimental measurements, the enantiomer composition could in principle be important if the hydration free energy is determined in part by a solubility measurement, because the solubility of a mixture of two enantiomers of a particular solute might be different than the solubility of either enantiomer alone. This mainly applies to racemic solids which form crystals with a racemic unit cell.

  2. Some participants raised concerns about the experimental data for mannitol, which was well predicted by some submissions but poorly predicted by others, and was by far the most polar compound in the set. Because of these concerns, we also re-computed statistics for all methods without mannitol, to see how much this would affect conclusions. However, we found that this did not dramatically change the rank-ordering of methods by most metrics, at least not more than would be expected given the (substantial) bootstrapped error bars. Thus, given the lack of any definitive evidence to the contrary, and the fact that mannitol was not one of the least well-predicted compounds, we kept mannitol in the set.

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Acknowledgments

We acknowledge the financial support of the National Institutes of Health (1R15GM096257-01A1), and computing support from the UCI GreenPlanet cluster, supported in part by NSF Grant CHE-0840513. We also thank J. Peter Guthrie for help with sorting out structure and naming confusion in SAMPL preparation, several SAMPL participants including Jens Reinisch and Samuel Genheden for helpful exchanges on issues with the guaiacol series, and Andreas Klamt for help on data relating to 1-benzylimidazole. We also thank OpenEye for their support of the SAMPL meeting and for running the web server, and Matt Geballe (OpenEye) for help managing the web site and automated submission system.

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Correspondence to David L. Mobley.

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Mobley, D.L., Wymer, K.L., Lim, N.M. et al. Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des 28, 135–150 (2014). https://doi.org/10.1007/s10822-014-9718-2

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