ArticleNature computational science2026
Revisiting molecular descriptors with TDiMS for interpretable intramolecular interactions based on substructure pairs.
Article in Nature computational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Revisiting molecular descriptors with TDiMS for interpretable intramolecular interactions based on substructure pairs.Nature computational science · 2026Article
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Authors and funding
7 authors.
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Abstract
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. Here, to address this issue, we present a 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 in predicting properties for which distances between substructures are a primary factor. In addition, these identified features are highly interpretable for experts in materials discovery.
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