Automated Retrosynthesis Planning of Macromolecules Using Large Language Models and Knowledge Graphs

TL;DR

This paper introduces an automated system combining large language models and knowledge graphs to plan retrosynthesis pathways for macromolecules, significantly advancing materials chemistry research automation.

Contribution

It presents the first fully automated retrosynthesis planning agent for macromolecules, integrating LLMs with a novel multi-branched pathway search algorithm.

Findings

Constructed complex retrosynthetic pathways for polyimide synthesis

Identified both known and novel reaction pathways

Demonstrated effectiveness of LLMs in literature-based pathway planning

Abstract

Identifying reliable synthesis pathways in materials chemistry is a complex task, particularly in polymer science, due to the intricate and often non-unique nomenclature of macromolecules. To address this challenge, we propose an agent system that integrates large language models (LLMs) and knowledge graphs. By leveraging LLMs' powerful capabilities for extracting and recognizing chemical substance names, and storing the extracted data in a structured knowledge graph, our system fully automates the retrieval of relevant literatures, extraction of reaction data, database querying, construction of retrosynthetic pathway trees, further expansion through the retrieval of additional literature and recommendation of optimal reaction pathways. By considering the complex interdependencies among chemical reactants, a novel Multi-branched Reaction Pathway Search Algorithm (MBRPS) is proposed to help identify all valid multi-branched reaction pathways, which arise when a single product decomposes into multiple reaction intermediates. In contrast, previous studies were limited to cases where a product decomposes into at most one reaction intermediate. This work represents the first attempt to develop a fully automated retrosynthesis planning agent tailored specially for macromolecules powered by LLMs. Applied to polyimide synthesis, our new approach constructs a retrosynthetic pathway tree with hundreds of pathways and recommends optimized routes, including both known and novel pathways. This demonstrates utilizing LLMs for literature consultation to accomplish specific tasks is possible and crucial for future materials research, given the vast amount of materials-related literature.