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Supplementary Section A provides full LLM prompts, Supplementary Section B provides details of case study analysis, Supplementary Section C provides an algorithmic overview of the human-in-the-loop procedure used to solve case study molecules, Supplementary Section D provides pseudocode for the DeepRetro top-level algorithm, Supplementary Section E provides pseudocode for the detailed LLM invocation algorithm, Supplementary Section F provides details of customization parameters and the open-source release, Supplementary Section G provides details and screenshots of the GUI, Supplementary Section H provides details of LLM API costs and cloud costs, Supplementary Section I provides details about automation bottlenecks, Supplementary Section J provides details about novelty assessment methodology, and Supplementary Section K provides details about scalability index Weaknesses of DeepRetro A notable challenge encountered during the development and application of our iterative LLM-Retrosynthesis pipeline pertains to the rate of chemically unsound or implausible suggestions generated by the LLM calls

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