We are laying the foundations for AI to enhance the design of new drugs and understanding existing medicines, eventually enabling AI to learn on its own. First, I describe FAIR (NeurIPS 2023), a generative model for protein pocket design that enhances drug binding to biological targets. FAIR co-designs protein pocket sequences and corresponding 3D structures, outperforming existing methods by 15.5% (AAR) and 13.5% (RMSD). For drugs to be effective, they must act on biological targets in relevant biological contexts. I describe PINNACLE (bioRxiv 2023), a multi-scale graph neural network for identifying optimal cell contexts for drugs to act in. PINNACLE models perform an array of tasks, including enhancing 3D structural protein representations critical in immune-oncology, predicting the effects of drugs across cell-type contexts, and nominating therapeutic targets in a cell-type specific manner. Finally, candidate drugs need to be matched to patient benefits. I present TxGNN (medRxiv 2023), a knowledge graph AI model for zero-shot prediction of therapeutic use across over 17,000 diseases, enabling drug repurposing for 7,000 rare diseases with a mere 5% having FDA-approved drugs. TxGNN's predictions align with clinical prescriptions across 1.2 million medical records. Last, we founded Therapeutics Commons, a global initiative to access and evaluate AI across therapeutic modalities (including small molecules, macro-molecules, cell and gene therapies) and stages of drug discovery (spanning from molecular design and target nomination to modeling efficacy, safety, and drug repurposing). The Commons offers benchmarks, leaderboards, and model hubs with pre-trained models and multimodal datasets to facilitate the use of AI in therapeutic science.