SAMPL6 Challenge Results from pKa Predictions Based on a General Gaussian Process Model

26 September 2018, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


A variety of fields would benefit from accurate pKa predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.
Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pKas of 24 drug like small molecules.
We recently built a general model for predicting pKas 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 pKas which are then used to analytically determine macroscopic pKas.
Our Gaussian process is trained on a set of 2,700 macroscopic pKas 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.


blind challenge
Gaussian Process

Supplementary materials

sampl6 SI

Supplementary weblinks


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.