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arXiv · 2605.10230

FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization

Qingchuan Zhang·He Cao·Hao Li·Yanjun Shao·Zhiyuan Liu·Shihang Wang·Shufang Xie·Shenghua Gao·Xinwu Ye
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Abstract

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.

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Qingchuan Zhang, He Cao, Hao Li, Yanjun Shao, Zhiyuan Liu, Shihang Wang, Shufang Xie, Shenghua Gao, Xinwu Ye. 2026-05-11. FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization. https://arxiv.org/abs/2605.10230

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