Publications
Feature combination networks for the interpretation of statistical machine learning models: application to Ames mutagenicity
A new algorithm has been developed to enable the interpretation of black box models for reactivity-based endpoints. The developed algorithm is agnostic to learning algorithm and descriptor choice, and has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.
