Researchers Train ML System Predicts Enantioselectivity
Researchers at the University of Utah and UCLA publish an accelerated preview in Nature on Feb. 11, 2026, describing an ML workflow that predicts enantioselectivity in nickel-based asymmetric cross-coupling reactions using data from four prior studies. The model accurately forecasts product handedness with limited experimental input, cutting typical screening from roughly 50–60 reactions to about 5–10, reducing time and cost for reaction optimization and pharmaceutical scale-up.
Scoring Rationale
High methodological novelty and strong experimental validation, limited to current nickel-based datasets though transferable in principle
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