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arXiv Machine Learning By Wenyu Zhu, Chengzhu Li, Xiaohe Tian, Yifan Wang, Yinjun Jia, Jianhui Wang, Bowen Gao, Haichuan Tan, Ya-Qin Zhang, Wei-Ying Ma, Yanyan Lan

Coder as Editor: Code-driven Interpretable Molecular Editing

Read the original on arXiv Machine Learning →

The paper introduces MECo, a framework that translates molecular editing intentions into executable code, bridging the gap between reasoning and execution in drug discovery. MECo generates human‑interpretable editing intentions from a molecule and design objective, then converts those intentions into precise structural edits. The method achieves over 98% accuracy on realistic edits and improves downstream benchmark consistency by 38–86 percentage points, outperforming SMILES‑based baselines while maintaining structural similarity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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