STAMP as a topology-aware molecular language for fragment-level learning and editing
Molecular fragments provide chemically meaningful units, but existing molecular languages rarely make their locations and connections explicit for editing. Here we introduce STAMP (Sequential Tree Attachment-based Motif-Preserving representation), a topology-aware molecular language that connects fragment-level representation learning with precise structural editing. STAMP separately encodes motif identity, attachment sites and tree topology, making fragments reusable across contexts and subtrees directly accessible. Learned representations capture motif similarity and recurring transformations, and lower activity-cliff prediction error on 23 of 30 targets versus a matched SMILES encoder....
| Authors | Yuxuan Ren, Jiatong Li, Ziqiao Meng et al. |
|---|---|
| Primary category | q-bio.QM |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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