An explainable molecular property prediction via multi-granularity

Molecular property prediction is an important task in drug discovery, especially the characterization of relationships between molecular substructures and their property. It is usually implemented by deep learning methods. However, deep model is a black-box that would lead to the prediction process and results are unbelievable and unreliable. To address these issues, researchers have begun to work on investigating explainable or substitute models for deep model. In this paper, an explainable framework of molecular property prediction with their multi-granularity representation (MgR) is proposed to characterize the contribution of substructures to prediction, called MgRX for short (‘X’ comes from ‘eXplainable’). Specifically, the MgRX is constructed to denote the substructures' contribution of different granularity, in which each substructure is progressively finer from top to bottom. The finest substructures' contribution to target is implemented by the deep learning model with SHAP. Besides, several experiments are executed to analyze the effectiveness of the multi-granularity framework to molecular property. Based on above discussions, we develop a basic framework of quantitative characterization on substructures' contribution to property via multi-granularity.