# C–H Borylation

Late-stage functionalization in medicinal chemistry is limited by scarce reaction data, especially when several similar C–H bonds compete for borylation. A Roche and ETH team used an XGBoost-ensemble active learning loop to pick 50 previously unreported substrates from a 22,253-molecule pool across three rounds of high-throughput experiments, expanding C–H borylation data to 6,865 reactions over 568 substrates. Geometric graph neural networks trained on that set beat XGBoost in most cases on random, Butina and Bemis–Murcko scaffold splits, and in a prospective test on ten unseen substrates with N-heteroaryl motifs the top predicted site matched experiment in all cases.

Late-stage functionalization in medicinal chemistry is limited by scarce reaction data, especially when several similar C–H bonds compete for borylation. A Roche and ETH team used an XGBoost-ensemble active learning loop to pick 50 previously unreported substrates from a 22,253-molecule pool across three rounds of high-throughput experiments, expanding C–H borylation data to 6,865 reactions over 568 substrates. Geometric graph neural networks trained on that set beat XGBoost in most cases on random, Butina and Bemis–Murcko scaffold splits, and in a prospective test on ten unseen substrates with N-heteroaryl motifs the top predicted site matched experiment in all cases.