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An Agentic Retrobiosynthesis Framework with Learned Frontier Selection

Research Bioinformatics AI

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TL;DR - This paper isolates the impact of LLM-guided search in rule-based retrobiosynthesis by keeping biochemical reaction generation fixed. A LoRA-tuned Qwen2.5-7B frontier-selection policy outperforms prompting and MCTS across multiple benchmarks, especially under constrained search budgets.

  • The LLM only selects the next frontier molecule to expand through a strict choice-only interface; a deterministic engine supplies identical validated biochemical transitions.
  • On LASER, the fine-tuned policy achieves a 65±1% solve rate with 10 expansions versus 59% for MCTS, and 78±1% versus 75% with 200 expansions.
  • At 200 expansions, it reaches 88±3% versus 80% on RetroPath RL Golden and 63±2% versus 45% on BioNavi-NP.
  • Gains depend on frontier construction and reaction ranking, indicating that search-policy improvements do not eliminate upstream design sensitivities.

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An Agentic Retrobiosynthesis Framework with Learned Frontier Selection

arXiv cs.CL Philippe Meyer, Guillaume Gricourt, Thomas Duigou, Joan Hérisson, Jean-Loup Faulon 2026-08-31 arXiv:2608.30702
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-15 14:23:33.673352 UTC

TL;DR - This paper isolates the impact of LLM-guided search in rule-based retrobiosynthesis by keeping biochemical reaction generation fixed. A LoRA-tuned Qwen2.5-7B frontier-selection policy outperforms prompting and MCTS across multiple benchmarks, especially under constrained search budgets.

  • The LLM only selects the next frontier molecule to expand through a strict choice-only interface; a deterministic engine supplies identical validated biochemical transitions.
  • On LASER, the fine-tuned policy achieves a 65±1% solve rate with 10 expansions versus 59% for MCTS, and 78±1% versus 75% with 200 expansions.
  • At 200 expansions, it reaches 88±3% versus 80% on RetroPath RL Golden and 63±2% versus 45% on BioNavi-NP.
  • Gains depend on frontier construction and reaction ranking, indicating that search-policy improvements do not eliminate upstream design sensitivities.
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