ABSTRACT AI is becoming a rapidly expanding area of synthetic organic chemistry with the ability to predict and plan reactions and to automate reactions. With the added value of chemical knowledge, existing artificial intelligence (AI) systems can now predict reaction products and selectivity, identify synthesis routes, and optimize reaction conditions more efficiently than ever before. Recent advances in the application of AI to synthetic chemistry are summarized, including predicting reactivity at the atomic level, modeling single‐step reactions, computer‐aided retrosynthetic planning, and laboratory automation. We cover key machine learning (ML) architectures, including neural networks (NNs), graph‐based models, and transformer architectures, and showcase their applications in reaction prediction, retrosynthesis, and drug synthesis. Particular emphasis is placed on the use of predictive models, together with automated experimental platforms, to enable closed‐loop optimization and autonomous development. Problems related to data quality, model interpretability, stereochemical control, and their application in synthesizing laboratories deserve research. Finally, we discuss the potential to achieve fully autonomous platforms and chemistry foundation models, as well as the long‐term possibilities of AI to reimagine synthetic methodology, expedite the discovery of new molecules, and enhance sustainability in organic chemistry.
Khuzaifa et al. (Mon,) studied this question.
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