An Agentic Retrobiosynthesis Framework with Learned Frontier Selection

The paper presents an agentic retrobiosynthesis framework that uses a learned frontier selection policy to guide multistep retrosynthesis in a deterministic biochemical engine. By fine‑tuning a Qwen2.5‑7B policy with LoRA, the authors achieve higher solve rates than traditional MCTS across several benchmarks, demonstrating that route‑supervised frontier selection can improve search efficiency without changing the underlying reaction model.

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• llms
• agents
• reinforcement-learning
• fine-tuning
• benchmarks