Prospective active transfer learning on the formal coupling of amines and carboxylic acids to form secondary alkyl bonds

Tailoring a reaction condition to suit new substrates can be labor-intensive. While machine learning can aid this endeavor, conventional strategies require large datasets to make useful predictions. Active transfer learning (ATL) tackles this problem by leveraging previously collected reaction data and adaptively selecting reagent combinations. Here, ATL is prospectively applied to find improved reagent combinations for C(sp³)-C(sp³) cross-couplings between activated amines and carboxylic acids. The formation of carbon-carbon bonds from amines and acids is a powerful complement to the classic amide coupling, but the formation of sterically congested secondary alkyl groups studied here represents a challenge for catalysis. Our results demonstrate ATL consistently improved yields within three batches of experiments, making the method of practical utility for chemical space exploration studies, such as drug discovery.