LLM-Augmented Chemical Synthesis and Design Decision Programs

• URL: http://arxiv.org/abs/2505.07027v1
• Date: Sun, 11 May 2025 15:43:00 GMT
• Title: LLM-Augmented Chemical Synthesis and Design Decision Programs
• Authors: Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, Chao Zhang,
• Abstract summary: We introduce an efficient scheme for encoding reaction pathways and present a new route-level search strategy.<n>We show that our LLM-augmented approach excels at retrosynthesis planning and extends naturally to the broader challenge of synthesizable molecular design.
• Score: 18.41721617026997
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug development. Although recent machine learning (ML) research has advanced single-step retrosynthetic modeling and subsequent route searches, these solutions remain restricted by the extensive combinatorial space of possible pathways. Concurrently, large language models (LLMs) have exhibited remarkable chemical knowledge, hinting at their potential to tackle complex decision-making tasks in chemistry. In this work, we explore whether LLMs can successfully navigate the highly constrained, multi-step retrosynthesis planning problem. We introduce an efficient scheme for encoding reaction pathways and present a new route-level search strategy, moving beyond the conventional step-by-step reactant prediction. Through comprehensive evaluations, we show that our LLM-augmented approach excels at retrosynthesis planning and extends naturally to the broader challenge of synthesizable molecular design.

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