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

2025-12-01

AI Summary

Current computer-aided synthesis planning (CASP) systems excel at generating tactical reaction steps but lack formal modeling capabilities for strategic principles—such as convergent assembly, protecting-group minimization, and cyclization sequence optimization. Method: We propose the first framework that formalizes organic synthesis strategy as verifiable, interpretable Python functions. Leveraging large language models, we extract implicit strategic knowledge from the literature to construct USPTO-ST, a strategically annotated synthesis route dataset, and develop an extensible library of strategy-aware rule functions. Our approach enables natural-language–driven route retrieval and quantitative evaluation aligned with strategic criteria. Contribution/Results: The framework bridges the tactical–strategic gap in CASP. In benchmarking, it achieves 75% top-3 route retrieval accuracy, supports chemically intuitive fine-grained clustering, and enables historical trend analysis—significantly enhancing strategic reasoning in CASP systems.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Extracting strategic insights from LLMs for organic synthesis planning
• Formalizing synthetic strategies into verifiable code functions
• Enabling strategic route retrieval and evaluation in CASP

Innovation

• Leverages LLMs to distill synthetic knowledge into code
• Translates strategic principles into Python functions
• Enables natural language-based route retrieval in CASP