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Automated Retrosynthesis Planning of Macromolecules Using Large Language Models and Knowledge Graphs
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This method proposes a comprehensive agent system that integrates LLMs for information extraction, entity alignment, and pathway evaluation/recommendation, with KGs for structured storage, efficient retrieval, and robust pathway construction and search. The system leverages the strengths of both to perform multi-step retrosynthesis planning, where LLMs and KGs collaboratively contribute to the complex reasoning process.
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This is a novel algorithm explicitly designed to identify all valid multi-branched reaction pathways within a retrosynthetic pathway tree derived from a knowledge graph. It functions as a core component of the overall agent, specifically aimed at helping LLMs overcome their limitations in complex, multi-branched reasoning, thereby enabling a more comprehensive and accurate reasoning process within the synergized framework.
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