Agentic Molecular Recovery via Molecule-Aware Exploration 文章

ArXiv CS.AI2026-06-06NEWSen作者: Suwan Yoon, Changhee Lee

摘要

arXiv:2606.05847v1 Announce Type: new Abstract: Text-guided molecular generation with LLMs often yields invalid SMILES. We argue that invalid drafts should be addressed through a shift from validity-oriented repair to identity-preserving molecular recovery: the objective is not only to restore chemical validity, but also to preserve target-relevant structural cues and recover the molecular identity implied by the description. This perspective reveals the limitations of existing correction strategies. Post-hoc repair can recover validity while distorting key structures, LLM-only correction can introduce unintended global drift, and generic agentic correction remains constrained by greedy single-candidate trajectories even when equipped with executable RDKit edit tools. To address these limitations, we propose AMREC, which couples molecule-aware mismatch tracking with expanded candidate exploration and trajectory-level selection.

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Agentic Molecular Recovery via Molecule-Aware Exploration
2026-06-06PRODUCT_LAUNCH影响: MEDIUM

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