Bridging Chemical Literature and Molecular Graphs: LLM‐Augmented Enantioselectivity Prediction for Iridium‐Catalyzed Asymmetric C–H Borylation

Asymmetric C-H borylation provides direct access to enantioenriched organoboron compounds. Predictive catalyst design for this reaction class remains challenging, as stereocontrol arises from coupled interactions among catalyst, substrate, and product structures. Moreover, mechanistic knowledge is dispersed across the literature. Here we report a machine-learning framework for predicting enantioselectivity in iridium-catalyzed asymmetric C-H borylation that bridges molecular graphs and chemical literature. A unified dataset comprising 800 literature-reported reactions was constructed for this reaction class. The model combines a multi-entity three-dimensional graph encoder for catalyst, substrate, and product with temperature-aware kinetic treatment, and a large-language-model prior module that extracts atom-resolved mechanistic annotations from primary publications. On a scaffold-based held-out test set, the STEREO-Lit (Spatial Three-Entity Reaction Encoder Literature) model achieves strong predictive performance, outperforming conventional machine-learning baselines and a geometry-grounded control. Perturbation studies further show that literature-derived semantic annotations contribute chemically meaningful, site-specific information rather than merely increasing model capacity. The trained model was subsequently applied to explore a reaction space of about 100 000 catalyst-substrate combinations and identify promising high-improvement candidates, several of which were supported by DFT calculations. The framework couples multi-entity 3D encoding with literature-derived priors, and remains predictive when training data are limited to a few hundred reactions.