{"ID":2854518,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14455","arxiv_id":"2510.14455","title":"Coder as Editor: Code-driven Interpretable Molecular Optimization","abstract":"Molecular optimization is a central task in drug discovery that requires precise structural reasoning and domain knowledge. While large language models (LLMs) have shown promise in generating high-level editing intentions in natural language, they often struggle to faithfully execute these modifications-particularly when operating on non-intuitive representations like SMILES. We introduce MECo, a framework that bridges reasoning and execution by translating editing actions into executable code. MECo reformulates molecular optimization for LLMs as a cascaded framework: generating human-interpretable editing intentions from a molecule and property goal, followed by translating those intentions into executable structural edits via code generation. Our approach achieves over 98% accuracy in reproducing held-out realistic edits derived from chemical reactions and target-specific compound pairs. On downstream optimization benchmarks spanning physicochemical properties and target activities, MECo substantially improves consistency by 38-86 percentage points to 90%+ and achieves higher success rates over SMILES-based baselines while preserving structural similarity. By aligning intention with execution, MECo enables consistent, controllable and interpretable molecular design, laying the foundation for high-fidelity feedback loops and collaborative human-AI workflows in drug discovery.","short_abstract":"Molecular optimization is a central task in drug discovery that requires precise structural reasoning and domain knowledge. While large language models (LLMs) have shown promise in generating high-level editing intentions in natural language, they often struggle to faithfully execute these modifications-particularly wh...","url_abs":"https://arxiv.org/abs/2510.14455","url_pdf":"https://arxiv.org/pdf/2510.14455v1","authors":"[\"Wenyu Zhu\",\"Chengzhu Li\",\"Xiaohe Tian\",\"Yifan Wang\",\"Yinjun Jia\",\"Jianhui Wang\",\"Bowen Gao\",\"Ya-Qin Zhang\",\"Wei-Ying Ma\",\"Yanyan Lan\"]","published":"2025-10-16T08:55:06Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"q-bio.BM\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}