AI agent framework improves retrosynthesis search with Qwen2.5-7B

Researchers have developed an agentic framework for retrosynthesis, a process used in drug discovery and chemical synthesis. This framework utilizes large language models, specifically Qwen2.5-7B, to select molecular frontiers for expansion. The fine-tuned Qwen2.5-7B policy demonstrated improved solve rates compared to Monte Carlo tree search on benchmarks like LASER and RetroPath RL Golden, indicating that route-supervised frontier selection can enhance budgeted search without altering the underlying biochemical generation process. AI

IMPACT This framework demonstrates how LLMs can enhance complex scientific search processes, potentially accelerating discovery in fields like drug development.

RANK_REASON The cluster contains an academic paper detailing a new AI framework for a scientific process. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• BioNavi-NP
• Escherichia coli
• Hugging Face
• LASER
• Monte Carlo tree search
• qwen2.5:7b
• RetroPath RL Golden

• paper
• model release

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