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Journal of Computer-Aided Molecular Design

, Volume 32, Issue 10, pp 1165–1177 | Cite as

SAMPL6 challenge results from \(pK_a\) predictions based on a general Gaussian process model

  • Caitlin C. Bannan
  • David L. Mobley
  • A. Geoffrey SkillmanEmail author
Article
  • 102 Downloads

Abstract

A variety of fields would benefit from accurate \(pK_a\) predictions, especially drug design due to the effect a change in ionization state can have on a molecule’s physiochemical properties. Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic \(pK_a\)s of 24 drug like small molecules. We recently built a general model for predicting \(pK_a\)s using a Gaussian process regression trained using physical and chemical features of each ionizable group. Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton. These features are fed into a Scikit-learn Gaussian process to predict microscopic \(pK_a\)s which are then used to analytically determine macroscopic \(pK_a\)s. Our Gaussian process is trained on a set of 2700 macroscopic \(pK_a\)s from monoprotic and select diprotic molecules. Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge. Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic. Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile–quantile plots, indicating it can predict its own accuracy. The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable.

Keywords

\(pK_a\) SAMPL6 Blind challenge Gaussian process 

Notes

Acknowledgements

DLM and CCB appreciate the financial support from the National Science Foundation (CHE 1352608) and the National Institutes of Health (1R01GM108889-01). CCB was supported financially by OpenEye Scientific Software to build this model during Summer 2017 and is now supported by a fellowship from The Molecular Sciences Software Institute under NSF Grant ACI-1547580. We are thankful for valuable conversations with OpenEye employees, the SAMPL6 organizers, and all challenge participants, and especially to Merck for its contributions to the experimental work in this challenge. AGS would like to thank Paul Hawkins, Christopher Bayly and Robert Tolbert as well as Anthony Nicholls and Matthew Geballe for many insightful discussions of \(pK_a\) and machine learning.

Supplementary material

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of ChemistryUniversity of CaliforniaIrvineUSA
  2. 2.2017 Summer Intern, OpenEye Scientific Software, Inc.Santa FeUSA
  3. 3.Departments of Pharmaceutical Sciences and ChemistryUniversity of CaliforniaIrvineUSA
  4. 4.OpenEye Scientific Software, Inc.Santa FeUSA

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