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 data set of 147 COM reactions with tiered predictive models to guide catalyst and substrate selection. A Morgan-fingerprint baseline enables rapid structure-based screening, while quantum-derived descriptor models achieve higher accuracy ( ≈ 0.92) and retain performance on external substrates; feature-importance and SHAP analyses identify catalyst HOMO energy, dimerization propensity, and substrate carbonyl polarization as dominant drivers of reactivity. Reactivity-cliff analysis distinguishes smoothly interpolating catalysts, such as FeCl and ZnCl, 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-4o-guided, code-free acquisition protocol offers a lower-barrier alternative that outperforms random sampling and, in this setting, exceeds expert selection. Together, these results establish a mechanistically interpretable, data-efficient framework for predicting, explaining, and prioritizing COM experiments in a controlled reaction matrix, and show how uncertainty-aware ML and LLM-guided selection can augment decision-making in mechanistically related COM systems.