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  1. Sparse reaction data gets a closed loop: active learning picks the experiments, and geometric GNNs call every borylation site on unseen substrates

    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.

  2. Chai Discovery

    Chai Discovery partners with Roche: Chai's AI models enter drug discovery programs across Roche's therapeutic areas

    Chai Discovery announced a collaboration with Roche to use Chai's AI models and platform in a number of drug discovery programs across Roche's therapeutic areas. Chai builds computer-aided design tools for protein-based therapeutics, and its latest model, Chai-3, is described by the company as achieving high target success rates and binding affinities, including against challenging targets. The deal is the latest in a series of 2026 collaborations Chai has announced with leading pharmaceutical companies; the announcement gives no program counts, specific targets or timelines.

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