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Carbohydrate Transformer: Predicting Regio- and Stereoselective Reactions Using Transfer Learning
Organic chemistry is central to society because it enables the synthesis of complex molecules and materials used in all fields of science and technology. The synthetic methods represent a vast body of accumulated knowledge optimally suited for deep learning. Indeed, most organic reactions involve distinct functional groups and can readily be learned by deep learning models and chemists alike. The task is, however, much more challenging for regio- and stereoselective transformations becausedoi:10.26434/chemrxiv.11935635.v1 fatcat:javud47ipfec7dowblctxh4jca