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Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections

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Abstract

The computation of distribution coefficients between polar and apolar phases requires both an accurate characterization of transfer free energies between phases and proper accounting of ionization and protomerization. We present a protocol for accurately predicting partition coefficients between two immiscible phases, and then apply it to 53 drug-like molecules in the SAMPL5 blind prediction challenge. Our results combine implicit solvent QM calculations with classical MD simulations using the non-Boltzmann Bennett free energy estimator. The OLYP/DZP/SMD method yields predictions that have a small deviation from experiment (RMSD = 2.3 \(\log\) D units), relative to other participants in the challenge. Our free energy corrections based on QM protomer and \({\text{p}}K_{\text{a}}\) calculations increase the correlation between predicted and experimental distribution coefficients, for all methods used. Unfortunately, these corrections are overly hydrophilic, and fail to account for additional effects such as aggregation, water dragging and the presence of polar impurities in the apolar phase. We show that, although expensive, QM-NBB free energy calculations offer an accurate and robust method that is superior to standard MM and QM techniques alone.

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Notes

  1. This is the version of Dunning’s DZP basis set that appears in the Psi4 quantum chemistry package [72].

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Acknowledgments

This work was supported by the intramural research program of the National Heart, Lung and Blood Institute of the National Institutes of Health and utilized the high-performance computational capabilities of the LoBoS and Biowulf Linux clusters at the National Institutes of Health. (http://www.lobos.nih.gov and http://biowulf.nih.gov)

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  1. Laboratory of Computational Biology, National Institutes of Health – National Heart, Lung and Blood Institute, 5635 Fishers Lane, T-900 Suite, Rockville, MD, 20852, USA

    Frank C. Pickard IV, Gerhard König, Florentina Tofoleanu, Juyong Lee, Andrew C. Simmonett & Bernard R. Brooks

  2. Max Planck Institut für Kohlenforschung, 45470, Mülheim an der Ruhr, NRW, Germany

    Gerhard König

  3. Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA

    Yihan Shao

  4. Department of Chemistry, Washington University, St. Louis, MO, 63130, USA

    Jay W. Ponder

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  1. Frank C. Pickard IV
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Pickard, F.C., König, G., Tofoleanu, F. et al. Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections. J Comput Aided Mol Des 30, 1087–1100 (2016). https://doi.org/10.1007/s10822-016-9955-7

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