Machine learning-assisted molecular design for efficient ¹⁹F hyperpolarization

Efficient ¹⁹F signal amplification by reversible exchange (SABRE) remains difficult because enhancement depends on multiple substrate-specific structural and electronic factors. Here we report a machine-learning-assisted strategy for identifying and ranking high-performance ¹⁹F SABRE substrates. A chemical space of more than 180 000 fluorinated N-heterocycles was mapped using refined molecular descriptors, and 33 representative fluorinated pyridine substrates were selected for experimental SABRE screening. Random forest classification identified N_Sum_of_connectivity as a key descriptor separating high- and low-enhancement substrates. For substrates classified in the high-enhancement regime, forward stepwise multivariate linear regression with leave-one-out cross-validation produced an interpretable five-descriptor model for quantitative signal-enhancement-factor prediction. External validation with six additional substrates showed good agreement between predicted and experimental values for high-enhancement candidates. Notably, 3-amino-4-fluoropyridine gave an experimental enhancement of 7133-fold, close to the predicted value of 6637-fold, corresponding to approximately 3.2% polarization at 1.4 T without co-ligands. The selected substrates further enabled single-scan simultaneous hyperpolarization of 15 fluorinated pyridines. This workflow provides an interpretable framework for data-driven optimization of ¹⁹F SABRE substrates.