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    <abstract>Existing LLM-based molecular optimization methods frame the task as molecule-to-molecule mapping, forcing models to implicitly learn structure-property relationships from paired SMILES alone. We argue this misses a key ingredient: task-level chemical knowledge that tells the model which structural motifs to prefer or avoid for a given property direction, before it ever looks at a specific molecule. We propose SkillMol, which constructs compact task skills – sets of recommended and discouraged structural motifs for each optimization objective – from domain knowledge, and pairs them with an atom-anchored action interface that decomposes editing into discrete, executable operations. Skills are used twice: as explicit training context so the model learns skill-conditioned edit actions, and as functional-group constraints during inference search. A deterministic RDKit executor applies the proposed graph edits. On the MoleculeSTM benchmark across 14 molecular editing tasks, SkillMol achieves 92.5% loose and 80.0% strict hit ratio on single-property tasks and 65.3% average across loose and strict multi-property columns, outperforming strong LLM baselines. Our code can be found at https://github.com/jiajunyu1999/SkillMol.</abstract>
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%0 Conference Proceedings
%T SkillMol: Skill-Conditioned Action Editing for Molecular Optimization
%A Yu, Jiajun
%A Li, Kun
%A Wu, Zhihao
%A Mao, Weifan
%A Zheng, Yizhen
%A Wang, Haishuai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yu-etal-2026-skillmol
%X Existing LLM-based molecular optimization methods frame the task as molecule-to-molecule mapping, forcing models to implicitly learn structure-property relationships from paired SMILES alone. We argue this misses a key ingredient: task-level chemical knowledge that tells the model which structural motifs to prefer or avoid for a given property direction, before it ever looks at a specific molecule. We propose SkillMol, which constructs compact task skills – sets of recommended and discouraged structural motifs for each optimization objective – from domain knowledge, and pairs them with an atom-anchored action interface that decomposes editing into discrete, executable operations. Skills are used twice: as explicit training context so the model learns skill-conditioned edit actions, and as functional-group constraints during inference search. A deterministic RDKit executor applies the proposed graph edits. On the MoleculeSTM benchmark across 14 molecular editing tasks, SkillMol achieves 92.5% loose and 80.0% strict hit ratio on single-property tasks and 65.3% average across loose and strict multi-property columns, outperforming strong LLM baselines. Our code can be found at https://github.com/jiajunyu1999/SkillMol.
%U https://aclanthology.org/2026.findings-emnlp.920/
%P 19428-19445

Markdown (Informal)

[SkillMol: Skill-Conditioned Action Editing for Molecular Optimization](https://aclanthology.org/2026.findings-emnlp.920/) (Yu et al., Findings 2026)

• SkillMol: Skill-Conditioned Action Editing for Molecular Optimization (Yu et al., Findings 2026)

ACL

• Jiajun Yu, Kun Li, Zhihao Wu, Weifan Mao, Yizhen Zheng, and Haishuai Wang. 2026. SkillMol: Skill-Conditioned Action Editing for Molecular Optimization. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19428–19445, Budapest, Hungary. Association for Computational Linguistics.