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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.14455 |
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| _version_ | 1866918161724473344 |
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| author | Zhu, Wenyu Li, Chengzhu Tian, Xiaohe Wang, Yifan Jia, Yinjun Wang, Jianhui Gao, Bowen Zhang, Ya-Qin Ma, Wei-Ying Lan, Yanyan |
| author_facet | Zhu, Wenyu Li, Chengzhu Tian, Xiaohe Wang, Yifan Jia, Yinjun Wang, Jianhui Gao, Bowen Zhang, Ya-Qin Ma, Wei-Ying Lan, Yanyan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14455 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coder as Editor: Code-driven Interpretable Molecular Optimization Zhu, Wenyu Li, Chengzhu Tian, Xiaohe Wang, Yifan Jia, Yinjun Wang, Jianhui Gao, Bowen Zhang, Ya-Qin Ma, Wei-Ying Lan, Yanyan Machine Learning Biomolecules 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. |
| title | Coder as Editor: Code-driven Interpretable Molecular Optimization |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2510.14455 |