Abstract
Theoretical approaches for predicting physicochemical properties are valuable tools for accelerating the drug discovery process. In this work, quantum chemical methods are used to predict water–octanol partition coefficients as a part of the SAMPL6 blind challenge. The SMD continuum solvent model was employed with MP2 and eight DFT functionals in conjunction with correlation consistent basis sets to determine the water–octanol transfer free energy. Several tactics towards improving the predictions of the partition coefficient were examined, including increasing the quality of basis sets, considering tautomerization, and accounting for inhomogeneities in the water and n-octanol phases. Evaluation of these various schemes highlights the impact of modeling approaches across different methods. With the inclusion of tautomers and adjustments to the permittivity constants, the best predictions were obtained with smaller basis sets and the O3LYP functional, which yielded an RMSE of 0.79 logP units. The results presented correspond to the SAMPL6 logP submission IDs: DYXBT, O7DJK, and AHMTF.
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Acknowledgements
The authors would like to thank Phillip S. Hudson, Andreas Krämer, and Andy Simmonett for their excellent dialogue and insight. We extend appreciation to Richard Venable, John Legato, Daniel Roe, and Rubén Meana Pañeda for technical assistance. This research was supported by the Intermural 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 (https://www.lobos.nih.gov and https://biowulf.nih.gov).
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Jones, M.R., Brooks, B.R. Quantum chemical predictions of water–octanol partition coefficients applied to the SAMPL6 logP blind challenge. J Comput Aided Mol Des 34, 485–493 (2020). https://doi.org/10.1007/s10822-020-00286-1
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DOI: https://doi.org/10.1007/s10822-020-00286-1