Revisiting Molecular Descriptors with TDiMS for Interpretable Intramolecular Interactions Based on Substructure Pairs

Molecular descriptors play a crucial role in representing the structural features of molecules for machine learning-based physical property prediction. However, current descriptors either consider only local aspects of molecular structures or fail to effectively learn nonlocal structural features involving long-distance intramolecular interactions. To address this issue, we present a new descriptor named TDiMS. TDiMS effectively summarizes the enumerated pairwise topological distances between molecular substructures, thus capturing nonlocal interactions. Our evaluation shows that TDiMS successfully identifies essential features of large structures and outperforms other representative descriptors. Additionally, these identified features are highly interpretable for experts in material discovery.