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Abstract
High throughput experimentation (HTE) is a common practice in the pharmaceutical industry. Medicinal chemists design reaction arrays to optimize the yield of couplings between building blocks and/or pharmacophores. Popular reactions attempted by medicinal chemists include the amide coupling, Suzuki coupling, and Buchwald-Hartwig coupling. We show how the artificial intelligence (AI) language model ChatGPT can automatically formulate reaction arrays for these common reactions based on the literature corpus it was trained on. Furthermore, we showcase how ChatGPT results can be directly translated into inputs for the HTE management software phactor, which enables automated execution and analysis of assays. This workflow is experimentally demonstrated.
