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Résumé : Organic synthesis relies heavily on the effective selection and optimization of catalysts, a process often limited by empirical and time-consuming trial-and-error approaches. This work introduces ChemReactLLM, a multimodal large language model designed to transform catalyst-driven organic reaction prediction and optimization. ChemReactLLM integrates a foundation language model with a specialized ReactAdapter for molecular structure embedding and a Text–Structure Cross-Attention Block that fuses textual experimental conditions with graph-based structural representations. Trained on a large multimodal dataset, the model predicts reaction outcomes, infers catalyst structure–activity relationships, and supports inverse catalyst design. Experiments demonstrate superior performance in yield prediction and robust generalization to unseen reactions. ChemReactLLM also provides mechanistic interpretability and chemically consistent reasoning, highlighting its potential to accelerate catalyst discovery and advance data-driven organic synthesis.