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Related Experiment Video

Updated: May 25, 2025

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

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

Qinyu Ma¹, Yuhao Zhou¹, Jianfeng Li¹

¹The State Key Laboratory of Molecular Engineering of Polymers, Research Center of Al for Polymer Science, Department of Macromolecular Science, Fudan University, Shanghai, 200433, China.

Macromolecular Rapid Communications

|February 27, 2025

Summary

This study introduces an automated agent using large language models (LLMs) and knowledge graphs to find polymer synthesis pathways. It enables efficient discovery of optimal reaction routes for macromolecules.

Keywords:

data mining knowledge graph large language model agent retrosynthesis planning

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Area of Science:

Materials Chemistry
Polymer Science
Computational Chemistry

Background:

Macromolecular nomenclature complexity hinders reliable synthesis pathway identification in polymer science.
Existing methods for retrosynthesis planning are often limited and not fully automated.

Purpose of the Study:

To develop a fully automated agent for retrosynthesis planning of macromolecules using large language models (LLMs) and knowledge graphs.
To address the challenges posed by complex and nonunique nomenclature in polymer synthesis.

Main Methods:

Integration of LLMs for chemical name recognition and data extraction.
Utilization of knowledge graphs for structured data storage and retrieval.
Development of a novel Multi-branched Reaction Pathway Search Algorithm (MBRPS) to identify all valid multi-branched reaction pathways.
Automated literature retrieval, reaction data extraction, database querying, and pathway construction.

Main Results:

The proposed agent successfully automates literature retrieval, reaction data extraction, and retrosynthesis pathway construction.
The novel MBRPS algorithm identifies multi-branched reaction pathways, overcoming limitations of previous single-intermediate approaches.
Application to polyimide synthesis generated a retrosynthetic pathway tree with hundreds of pathways, recommending optimized routes.

Conclusions:

This work presents the first fully automated retrosynthesis planning agent for macromolecules powered by LLMs.
The developed system enhances the efficiency and scope of identifying reliable synthesis pathways in polymer chemistry.
The approach facilitates the discovery of both known and novel synthetic routes for complex polymers.