¹⁹F NMR-Guided Chiral Analysis and Machine Learning for Absolute Configuration Prediction of Carboxylic Acids

19F-labeled molecular probes offer significant advantages in chiral analysis, with their stereostructures playing a critical role in enantiodiscrimination. Rapid and accurate assignment of absolute configurations is particularly important for the pharmaceutical field and asymmetric synthesis. This work presents a comparative evaluation of two 19F-labeled probes as CDAs: aromatic P1 ((S)-1-(2-fluorophenyl)ethylamine) and aliphatic P2 (trans-2-fluorocyclohexanamine), focusing on their enantiomeric recognition of chiral carboxylic acids. The results demonstrate that P1 outperformed P2, particularly in analyzing carboxylic acids with chiral centers positioned several carbons away from the carboxyl group or possessing multiple chiral centers. Density functional theory calculations elucidated the underlying mechanisms, revealing that P1's great recognition efficiency arises from a larger energy difference between diastereomers and the formation of hydrogen bonds between its fluorine atom and the analytes' protons. These findings emphasize the importance of probe-analyte interactions in CDA-based enantiorecognition systems for achieving high-resolution discrimination. Moreover, P1 exhibited practical applications in determining ee values and analyzing various brands of ibuprofen capsules and tablets, highlighting its potential for pharmaceutical quality control. Furthermore, we introduced a novel online platform combining a P1-guided 19F NMR approach with machine learning for automated prediction of absolute configurations. This work sheds light on the differences between aromatic and aliphatic probes in enantiorecognition, while introducing an online predictive tool, in favor of the design of 19F molecular probes and the rapid assignment of absolute configurations.