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Updated: Jul 8, 2026

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Leveraging Bayesian Optimization for Accelerated Ligand Selection in Atroposelective Negishi Coupling
Yujing Zhou1, Richard C Walroth1, Lin Deng1
1Department of Synthetic Molecule Process Chemistry, Genentech, Inc., South San Francisco, California 94080, United States.
None:
We report a descriptor-informed Bayesian optimization strategy for accelerating ligand selection in asymmetric catalysis, demonstrated in a highly atroposelective Negishi cross-coupling reaction en route to a KRAS G12C inhibitor. Whereas multivariate linear regression failed to capture this complex catalytic landscape under sparse-data conditions, integration of forward-step descriptor selection with DFT-derived descriptors enabled efficient optimization of yield and diastereoselectivity, revealing the underexploited reactivity of Phanephos ligands that delivered the target atropisomer in 84% yield and 91:9 dr.
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