Coder as Editor: Code-driven Interpretable Molecular Optimization

• URL: http://arxiv.org/abs/2510.14455v1
• Date: Thu, 16 Oct 2025 08:55:06 GMT
• Title: Coder as Editor: Code-driven Interpretable Molecular Optimization
• Authors: Wenyu Zhu, Chengzhu Li, Xiaohe Tian, Yifan Wang, Yinjun Jia, Jianhui Wang, Bowen Gao, Ya-Qin Zhang, Wei-Ying Ma, Yanyan Lan,
• Abstract summary: We introduce MECo, a framework that bridges reasoning and execution by translating editing actions into executable code.<n>Our approach achieves over 98% accuracy in reproducing held-out realistic edits from chemical reactions and target-specific compound pairs.
• Score: 36.84386817559159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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