SynthStrategy: Extracting and Formalizing Latent Strategic Insights from LLMs in Organic Chemistry

TL;DR

This work introduces SynthStrategy, a framework that distills tacit strategic knowledge from large language models into an extensible library of executable Python functions. By converting multi-step synthetic strategy into testable code, it enables strategy-based retrieval, clustering, and evaluation in computer-assisted synthesis planning (CASP). The authors release the USPTO-ST dataset and demonstrate that strategy-aware retrieval and clustering provide more nuanced organization and scalable, interpretable reasoning than traditional template- or topology-based approaches. Temporal trend analyses on patent data and case studies underscore the practical value of explicit strategic representations for guiding route selection. Overall, this approach advances CASP toward planning that more closely mirrors expert chemists' strategic thinking while maintaining computational efficiency and transparency.

Abstract

Modern computer-assisted synthesis planning (CASP) systems show promises at generating chemically valid reaction steps but struggle to incorporate strategic considerations such as convergent assembly, protecting group minimization, and optimal ring-forming sequences. We introduce a methodology that leverages Large Language Models to distill synthetic knowledge into code. Our system analyzes synthesis routes and translates strategic principles into Python functions representing diverse strategic and tactical rules, such as strategic functional group interconversions and ring construction strategies. By formalizing this knowledge as verifiable code rather than simple heuristics, we create testable, interpretable representations of synthetic strategy. We release the complete codebase and the USPTO-ST dataset -- synthesis routes annotated with strategic tags. This framework unlocks a novel capability for CASP: natural language-based route retrieval, achieving 75\% Top-3 accuracy on our benchmark. We further validate our library through temporal analysis of historical trends and chemically intuitive route clustering that offers more granular partitioning than common previous methods. This work bridges the tactical-strategic divide in CASP, enabling specification, search, and evaluation of routes by strategic criteria rather than structure alone.

Figures (7)

• Figure 1: Overview of the SynthStrategy framework for distilling latent chemical knowledge from large language models into a verifiable library of strategy functions. The process involves extracting strategy descriptions from LLM analysis of synthesis routes, generating executable Python functions, and refining them through testing and iteration. Applications include: (A) Creation of the strategically annotated USPTO-ST dataset; (B) Temporal analysis of evolving synthetic strategies in the USPTO dataset; (C) Strategy-based clustering of synthesis pathways for chemically intuitive grouping; (D) Natural language-based retrieval of strategy-matched routes from large corpora.
• Figure 2: Here we show three examples from our retrieval benchmark. In Queries A and B, the ground truth routes are returned as the Rank 1 result. For Query C, the retrieval system fails to correctly find the desired route at Rank 1, failing to match the request for a route which preserves a pyrazole from the starting materials ( instead finding a match for 2 out of 3 desired heterocycles ) and uses a Sonogashira coupling to introduce an alkyl linker via alkyne reduction. However, the system does find the ground truth route at Rank 3.
• Figure 3: Temporal analysis of synthetic strategies in the USPTO dataset. (a) The presence versus formation rates of spirocyclic compounds in USPTO routes has seen a marked increase over the past 5 years. (b) In orange we see the Azide to amine Staudinger reduction while in magenta, the rise in use of 1,3 cycloadditions to form triazoles is observed post 2007. (c) The percentage of routes in which a function representing heterocycle preservation from the starting material returns True has steadily increased, while strategies involving de novo ring construction via cyclization have stagnated. (d) This trend is mirrored by the dramatic rise of Suzuki Coupling, which facilitates the connection of pre-built molecular fragments, coinciding with the relative decline of traditional linker reactions.
• Figure 4: Here we show cluster representatives of clustering synthesis pathways to an ORL1 Antagonist. Strategy cluster representatives are numbered and grouped within boxes. TED clusters are denoted by colour. Strategy Clustering used KMeans and returned K=5 and TED Clustering used Agglomerative clustering and returned K=3 for an optimal number of clusters. We note that out of seven total routes were generated by AiZynthFinder, but for clarity not all are shown here.
• Figure 5: Two representative strategy functions rated 'Perfect' by our LLM evaluator. (a) This function detects a four-step sequence for masking an amine as an azide. (b) This function identifies a redox manipulation strategy, ensuring both the presence and correct temporal ordering of an ester reduction and an alcohol oxidation.