Practical Data-Driven Interrogation of Reactivity in Acid-Catalyzed Carbonyl–Olefin Metathesis with Machine Learning and Large Language Models

Carbonyl–olefin metathesis (COM) has emerged as a powerful yet mechanistically complex transformation for forging carbon–carbon bonds. Although diverse Brønsted and Lewis acid catalysts enable COM reactivity, predicting which catalyst will be effective for a given substrate remains challenging. Likewise, machine learning (ML) and large language models (LLMs) are emerging tools for planning experiments in organic synthesis, but their relative strengths in mechanistically rich catalytic systems like COM remain unclear. Here we address both challenges through a practical ML framework that combines a rigorously curated, blinded dataset of 147 COM reactions—spanning structurally diverse substrates and Brønsted, Lewis, and superelectrophilic ion-pair catalysts—with tiered predictive models to guide catalyst and substrate selection. A Morgan-fingerprint baseline captures substantial yield variation, demonstrating that structure-only representations provide rapid initial screening. Physically informed models based on quantum-derived descriptors of independent catalyst and substrate structures achieve higher accuracy (R² ≈ 0.92) and maintain performance on external substrates, while feature-importance and SHAP analyses reveal catalyst HOMO energy, dimerization propensity, and substrate carbonyl polarization as dominant determinants of reactivity. Reactivity-cliff analysis distinguishes smoothly interpolating catalysts such as FeCl₃ and ZnCl₂ from cliff-rich ion-pair systems, identifying regions where predictions are intrinsically unreliable and targeted experiments are most informative. Variance-guided Bayesian optimization recovers most full-model performance using only ~40% of the reaction matrix, while a parallel GPT-4–driven, code-free acquisition protocol surpasses expert selection and substantially outperforms random sampling. Together, these results provide a mechanistically interpretable, data-efficient blueprint for COM catalyst discovery and demonstrate how uncertainty-aware ML and LLMs can augment human intuition in complex catalytic systems.

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