Challenging enantio- and diastereoselective cobalt-catalyzed C–H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C–H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C–N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.

具有挑战性的对映选择性和非对映选择性钴催化的 C-H 烷基化已通过创新的数据驱动知识转移策略实现。利用相关转换的统计数据作为知识源，设计的机器学习 (ML) 模型利用增量学习并实现准确和外推的对映选择性预测。在知识转移模型的支持下，对 360 种手性羧酸的广泛虚拟筛选导致发现了一种具有有趣呋喃基部分的新型催化剂。进一步的实验证实，预测的手性羧酸可以对目标 C-H 烷基化实现出色的立体化学控制，这支持用C快速合成大型取代吲哚库-中心和 C-N 轴向手性。报告的机器学习方法提供了一个强大的数据引擎，通过利用可用结构性能统计数据的隐藏价值来加速分子催化的发现。