Transition-metal-catalyzed direct C-3 arylation of imidazo[1,2-a]pyridines has witnessed significant advances in recent years. However, identifying optimal reaction conditions for this transformation relies heavily on labor-intensive trial-and-error experiments, which consume significant time and costs. To address this challenge, 275 valid reaction data entries conforming to the reaction mechanism were collected as training data, including substrates, catalysts, additives, solvents, reaction temperature, and experimental yield. Three mainstream tree-based ensemble regression models, including GradientBoosting, Random Forest and ExtraTrees, were constructed after strict data cleaning and advanced feature engineering for systematic benchmark comparison. The GradientBoosting model, which delivered the best fitting and generalization performance among all candidates, was selected as the objective function for subsequent constrained Bayesian optimization. A constrained Bayesian optimization algorithm (with predicted yield bounded within 0–100%) was adopted to globally screen high-dimensional reaction parameters for maximum yield. Comprehensive cross-model evaluation indicated the test-set determination coefficients followed the order GradientBoosting (R2 = 0.4416) > Random Forest (R2 = 0.3864) > ExtraTrees (R2 = 0.3544), while the root mean square error (RMSE) of GradientBoosting was 15.3144, outperforming the other two models in prediction stability. Under the optimal parameter set recommended by GradientBoosting (catalyst Pd(acac)2BuAd2P, reactants ref-5-1d and ref-9-2d, additive CsOAc, solvent DMA, 150 °C), the model predicted a theoretical maximum yield of 100%, whereas Random Forest and ExtraTrees only reached predicted peak yields of 83.63% and 80.80%, respectively. This multi-model benchmarking workflow eliminates the bias risk from single-model prediction, provides an efficient tool for optimizing metal-catalyzed C-3 arylation of imidazo[1,2-a]pyridines, and can be generalized to other complex transition-metal catalytic systems.
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Zeng et al. (2026) studied this question.
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