2024 AIChE Annual Meeting
(196g) Autonomous Faithful Retrosynthesis with Large Language Models: From Synthesis Planning to Experimental Procedures
Authors
Herein, we introduce a novel LLM-based intelligent agent named FaithRetro, designed to accurately predict precursors, deduce reaction conditions, and draft experimental procedures. FaithRetro leverages its capabilities to understand reaction templates, navigate through local reaction databases, and assess experimental procedures, thereby facilitating the generation of reliable retrosynthesis routes and experimental protocols. The efficacy and adaptability of FaithRetro were evaluated across various tasks, including precursor prediction for target molecules, efficient retrieval of relevant experimental procedures, and the assessment and prioritization of experimental procedures based on criteria such as action complexity, reagent toxicity, and reaction duration. The results demonstrate FaithRetro’s capacity to innovate upon existing procedures based on user-defined criteria. Lastly, FaithRetro is asked to autonomously predict precursors and generate practical experimental procedures with reference for a prompt including a target molecule and given procedure constraints. The findings suggest that FaithRetro as an intelligent system significantly broadens the applicability of LLMs in retrosynthesis, markedly reducing human intervention and simplifying the process of content verification by ensuring the prediction of faithful content with reference.
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