Interpretable machine learning as a tool for scientific discovery in chemistry

Abstract

There has been an upsurge of interest in applying machine-learning (ML) techniques to chemistry, and a number of these applications have achieved impressive predictive accuracies; however, they have done so without providing any insight into what has been learnt from the training data. The interpretation of ML systems (i.e., a statement of what an ML system has learnt from data) is still in its infancy, but interpretation can lead to scientific discovery, and examples of this are given in the areas of drug discovery and quantum chemistry. It is proposed that a research programme be designed that systematically compares the various model-agnostic and model-specific approaches to interpretable ML within a range of chemical scenarios.