We transcend traditional structure-based analyses by embedding molecules into a unified bioactivity–chemical knowledge space. This integrated representation enables us to interpret a compound’s properties in relation to its biological outcomes, strengthening predictive accuracy and mechanistic interpretability.

We design and deploy cutting-edge computational approaches, including multimodal machine learning, deep neural networks, and emerging large language model frameworks. Our methods resolve hidden structure–function relationships and accelerate the discovery of novel therapeutics, safety liabilities, and disease mechanisms.