ArticleRSC advances2026
Global descriptors informed learning for organic melting-point prediction.
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Machine learning-assisted melting-point prediction remains challenging because topology-derived representations can not explicitly encode all whole-molecule physicochemical factors. Herein, we evaluate a global-informed framework by incorporating efficient molecular physical descriptors through a conditioning branch, achieving MAEs of 21.45 ± 0.08 °C and 23.06 ± 0.25 °C across five random-split and five scaffold-split folds. A matched comparison with the topology-only model shows a small but consistent contribution from the descriptor branch under scaffold-aware validation. This work highlights the value of global physicochemical information in complementing topology-derived representations for physical-property prediction and of evaluation under reduced structural overlap.
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Registered trials
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