@inproceedings{zhu-etal-2026-coder,
title = "Coder as Editor: Code-driven Interpretable Molecular Editing",
author = "Zhu, Wenyu and
Li, Chengzhu and
Tian, Xiaohe and
Wang, Yifan and
Jia, Yinjun and
Wang, Jianhui and
Gao, Bowen and
Tan, Haichuan and
Zhang, Ya-Qin and
Ma, Wei-Ying and
Lan, Yanyan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.248/",
pages = "5216--5236",
ISBN = "979-8-89176-462-0",
abstract = "Molecular design 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 modification 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 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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<abstract>Molecular design 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 modification 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 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.</abstract>
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%0 Conference Proceedings
%T Coder as Editor: Code-driven Interpretable Molecular Editing
%A Zhu, Wenyu
%A Li, Chengzhu
%A Tian, Xiaohe
%A Wang, Yifan
%A Jia, Yinjun
%A Wang, Jianhui
%A Gao, Bowen
%A Tan, Haichuan
%A Zhang, Ya-Qin
%A Ma, Wei-Ying
%A Lan, Yanyan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhu-etal-2026-coder
%X Molecular design 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 modification 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 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.
%U https://aclanthology.org/2026.findings-emnlp.248/
%P 5216-5236
Markdown (Informal)
[Coder as Editor: Code-driven Interpretable Molecular Editing](https://aclanthology.org/2026.findings-emnlp.248/) (Zhu et al., Findings 2026)
ACL
- Wenyu Zhu, Chengzhu Li, Xiaohe Tian, Yifan Wang, Yinjun Jia, Jianhui Wang, Bowen Gao, Haichuan Tan, Ya-Qin Zhang, Wei-Ying Ma, and Yanyan Lan. 2026. Coder as Editor: Code-driven Interpretable Molecular Editing. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 5216–5236, Budapest, Hungary. Association for Computational Linguistics.