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High-throughput experimentation is a common practice in the optimization of chemical synthesis. Chemists design reaction arrays to optimize the yield of couplings between building blocks. Popular reactions used in pharmaceutical research 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. Critically, we showcase how ChatGPT results can be directly translated into inputs for the management software phactor, which enables automated execution and analysis of assays. This workflow is experimentally demonstrated, with modest to excellent yields of products obtained in each instance on the first attempt.
Mahjour et al. (Tue,) studied this question.