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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\u2010learning framework for predicting enantioselectivity in iridium\u2010catalyzed asymmetric C\u2013H borylation that bridges molecular graphs and chemical literature. A unified dataset comprising 800 literature\u2010reported reactions was constructed for this reaction class. The model combines a multi\u2010entity three\u2010dimensional graph encoder for catalyst, substrate, and product with temperature\u2010aware kinetic treatment, and a large\u2010language\u2010model prior module that extracts atom\u2010resolved mechanistic annotations from primary publications. On a scaffold\u2010based held\u2010out test set, the STEREO\u2010Lit (Spatial Three\u2010Entity Reaction Encoder Literature) model achieves strong predictive performance, outperforming conventional machine\u2010learning baselines and a geometry\u2010grounded control. Perturbation studies further show that literature\u2010derived semantic annotations contribute chemically meaningful, site\u2010specific information rather than merely increasing model capacity. The trained model was subsequently applied to explore a reaction space of about 100\u00a0000 catalyst\u2013substrate combinations and identify promising high\u2010improvement candidates, several of which were supported by DFT calculations. The framework couples multi\u2010entity 3D encoding with literature\u2010derived priors, and remains predictive when training data are limited to a few hundred reactions.<\/jats:p>","DOI":"10.1002\/advs.78094","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T05:57:23Z","timestamp":1792043843000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging Chemical Literature and Molecular Graphs: LLM\u2010Augmented Enantioselectivity Prediction for Iridium\u2010Catalyzed Asymmetric C\u2013H Borylation"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6719-6800","authenticated-orcid":false,"given":"Zhe","family":"Wang","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1474-7841","authenticated-orcid":false,"given":"Baohua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Bay Laboratory Shen Zhen China"},{"name":"Shenzhen Medical Academy of Research and Translation Shen Zhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5599-0975","authenticated-orcid":false,"given":"Yi Isaac","family":"Yang","sequence":"additional","affiliation":[{"name":"Shenzhen Bay Laboratory Shen Zhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5814-1162","authenticated-orcid":false,"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3472-3413","authenticated-orcid":false,"given":"Yinwu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Materials and New Energy South China Normal University Shan Wei 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