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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.10230 |
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| _version_ | 1866916000211927040 |
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| author | Zhang, Qingchuan Cao, He Li, Hao Shao, Yanjun Liu, Zhiyuan Wang, Shihang Xie, Shufang Gao, Shenghua Ye, Xinwu |
| author_facet | Zhang, Qingchuan Cao, He Li, Hao Shao, Yanjun Liu, Zhiyuan Wang, Shihang Xie, Shufang Gao, Shenghua Ye, Xinwu |
| contents | Molecular optimization seeks to improve a molecule through small structural edits while preserving similarity to the starting compound. Recent language-model approaches typically treat this task as prompt-conditioned sequence generation. However, relying on natural language introduces an inherent data-scaling bottleneck, often leads to chemical hallucinations, and ignores the strong context dependence of fragment effects. We present FORGE, a two-stage framework that reformulates molecular optimization as context-aware local editing. By utilizing automatically mined, verified low-to-high edit pairs instead of expensive human text annotations, Stage 1 ranks candidate fragments by their property contribution under the full molecular context to inject chemical prior, and Stage 2 generates explicit fragment replacements. Built on a compact 0.6B language model, FORGE further adapts to unseen black-box objectives through in-context demonstrations. Across Prompt-MolOpt, PMO-1k and ChemCoTBench, FORGE consistently outperforms prior methods, including substantially larger language models and graph methods. These results highlight the value of explicit fragment-level supervision as a more easily obtainable, scalable, and hallucination-less alternative to natural language training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10230 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization Zhang, Qingchuan Cao, He Li, Hao Shao, Yanjun Liu, Zhiyuan Wang, Shihang Xie, Shufang Gao, Shenghua Ye, Xinwu Machine Learning Molecular optimization seeks to improve a molecule through small structural edits while preserving similarity to the starting compound. Recent language-model approaches typically treat this task as prompt-conditioned sequence generation. However, relying on natural language introduces an inherent data-scaling bottleneck, often leads to chemical hallucinations, and ignores the strong context dependence of fragment effects. We present FORGE, a two-stage framework that reformulates molecular optimization as context-aware local editing. By utilizing automatically mined, verified low-to-high edit pairs instead of expensive human text annotations, Stage 1 ranks candidate fragments by their property contribution under the full molecular context to inject chemical prior, and Stage 2 generates explicit fragment replacements. Built on a compact 0.6B language model, FORGE further adapts to unseen black-box objectives through in-context demonstrations. Across Prompt-MolOpt, PMO-1k and ChemCoTBench, FORGE consistently outperforms prior methods, including substantially larger language models and graph methods. These results highlight the value of explicit fragment-level supervision as a more easily obtainable, scalable, and hallucination-less alternative to natural language training. |
| title | FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.10230 |