SAMPL6 challenge results from [Formula: see text] predictions based on a general Gaussian process model.

ABSTRACT: A variety of fields would benefit from accurate [Formula: see text] 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 [Formula: see text]s of 24 drug like small molecules. We recently built a general model for predicting [Formula: see text]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 [Formula: see text]s which are then used to

SUBMITTER: Bannan CC 

PROVIDER: S-EPMC6438616 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature