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LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules

Abstract

This work presents a quantum mechanical model for predicting octanol-water partition coefficients of small protein-kinase inhibitor fragments as part of the SAMPL6 LogP Prediction Challenge. The model calculates solvation free energy differences using the M06-2X functional with SMD implicit solvation and the def2-SVP basis set. This model was identified as dqxk4 in the SAMPL6 Challenge and was the third highest performing model in the physical methods category with 0.49 log Root Mean Squared Error (RMSE) for predicting the 11 compounds in SAMPL6 blind prediction set. We also collaboratively investigated the use of empirical models to address model deficiencies for halogenated compounds at minimal additional computational cost. A mixed model consisting of the dqxk4 physical and hdpuj empirical models found improved performance at 0.34 log RMSE on the SAMPL6 dataset. This collaborative mixed model approach shows how empirical models can be leveraged to expediently improve performance in chemical spaces that are difficult for ab initio methods to simulate.

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Acknowledgements

We acknowledge the National Institutes of Health for supporting the experimental work carried out in the SAMPL6 logP Prediction Challenge.

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Correspondence to Slade Matthews.

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Guan, D., Lui, R. & Matthews, S. LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules. J Comput Aided Mol Des 34, 511–522 (2020). https://doi.org/10.1007/s10822-020-00278-1

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  • DOI: https://doi.org/10.1007/s10822-020-00278-1

Keywords

  • SAMPL6
  • LogP
  • Computational chemistry
  • Implicit solvation
  • DFT