A Perspective on Explanations of Molecular Prediction Models

Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of interpretability in black-box models. Explainable artificial intelligence (XAI) is a branch of artificial intelligence (AI) which addresses this drawback by providing tools to interpret DL models and their predictions. We review the principles of XAI in the domain of chemistry and emerging methods for creating and evaluating explanations. Then, we focus on methods developed by our group and their applications in predicting solubility, blood-brain barrier permeability, and the scent of molecules. We show that XAI methods like chemical counterfactuals and descriptor explanations can explain DL predictions while giving insight into structure-property relationships. Finally, we discuss how a two-step process of developing a black-box model and explaining predictions can uncover structure-property relationships.

Automated summary

Chemists are skeptical about using deep learning (DL) for decision making due to lack of interpretability in black-box models. Explainable artificial intelligence (XAI) addresses this issue by providing tools to interpret DL models and predictions. This article reviews XAI principles in chemistry, introduces emerging methods, and focuses on our group's methods and their applications in predicting solubility, blood-brain barrier permeability, and scent of molecules. XAI methods like chemical counterfactuals and descriptor explanations can explain DL predictions and provide insights into structure-property relationships. The two-step process of developing a black-box model and explaining predictions can uncover structure-property relationships.