Capturing Long-Range Intramolecular Interactions with TDiMS for Interpretable Property Prediction

Long-range intra-molecular interactions are not well represented by existing molecular descriptors, which limits the accuracy of machine learning models for molecular property prediction. We introduce TDiMS, a descriptor that encodes topological distances between substructure pairs, enabling explicit representation of long-range effects while retaining chemical meaning. Applied to molecular datasets, TDiMS shows particular advantages for larger molecules, where long-range interactions strongly influence target properties. We further demonstrate that choosing appropriate substructure definitions, such as tailored fragments, enhances predictive performance. Beyond accuracy, TDiMS provides interpretable features essential for material discovery, offering insights into structural motifs driving predictions. These results highlight distance-based, interpretable descriptors as a promising route for machine learning in the materials discovery.