<p>Designing effective catalysts is a key process for optimizing catalytic reactions to reduce time and waste during scale-up. Recently proposed approaches, including generative models, show promise in identifying new catalysts. However, they are mostly developed for specific reaction classes and predefined fragment categories without considering reaction components, limiting the exploration of novel catalysts across reaction space. Here, we present CatDRX, a catalyst discovery framework powered by a reaction-conditioned variational autoencoder generative model for generating catalysts and predicting their catalytic performance. The model is pre-trained on a broad reaction database and fine-tuned for downstream reactions. Our approach achieves competitive performance in both yield and related catalytic activity prediction. Additionally, it enables effective generation of potential catalysts given reaction conditions by integrating optimization toward desired properties and validation based on reaction mechanisms and chemical knowledge, as demonstrated in various case studies. This work helps facilitate and advance catalyst design and discovery for chemical and pharmaceutical industries.</p><p></p>