Agentic Molecular Recovery via Molecule-Aware Exploration

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computational Chemistry & Molecular AI · Depth: Expert, medium

Summary

Text-guided molecular generation using Large Language Models (LLMs) frequently produces invalid SMILES strings. Existing methods for correcting these drafts, such as post-hoc repair or LLM-only correction, often distort key structures or introduce unintended global changes, while generic agentic correction is limited by greedy single-candidate trajectories. To overcome these issues, researchers propose AMREC, an agentic framework for molecular recovery. AMREC combines grounded molecular editing with molecule-aware reasoning and expanded candidate exploration. It derives explicit structural requirements from target descriptions and uses Checker, Critic, and Planner agents to track semantic mismatches. By building and revisiting multiple recovery candidates, AMREC achieves the strongest overall recovery profile across structural, exact-match, and string-level metrics on invalid ChEBI-20 drafts from three backbone models.

Key takeaway

For Machine Learning Engineers developing text-guided molecular generation systems, recognize that invalid SMILES drafts often contain meaningful chemical information. Instead of discarding them or applying simple validity repair, consider implementing agentic recovery frameworks like AMREC. This approach, which preserves structural cues and explores multiple candidates, significantly improves the restoration of intended molecular identities, reducing wasted computational effort and enhancing the utility of LLM-generated outputs.

Key insights

Molecular recovery should prioritize identity preservation and semantic alignment over mere validity restoration for invalid SMILES.

Principles

• Recovery requires semantic and context-aware restoration.
• Greedy single-candidate search limits agentic correction.
• Explicitly track molecule-text mismatches.

Method

AMREC uses Checker, Critic, and Planner agents to track semantic mismatches against derived structural requirements. It employs a Candidate Explorer to build, retain, and revisit multiple recovery candidates, moving beyond greedy single-candidate trajectories.

In practice

• Use RDKit-based executable edit tools for grounded actions.
• Implement multi-candidate exploration for robust recovery.
• Derive verifiable structural requirements from descriptions.

Topics

• Molecular Generation
• SMILES
• LLM Agents
• Molecular Recovery
• RDKit
• Chemical Informatics

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.