We envision "AI scientists" as collaborative systems that learn, reason, and interact across diverse biomedical tools and data modalities to accelerate scientific discovery. These agents integrate foundation models, real-time knowledge, and structured experimentation to solve complex problems in therapeutics and biology. I will present two such systems: ProCyon and TxAgent. ProCyon is a foundation model for protein phenotypes, trained across five interrelated knowledge domains: molecular function, therapeutic mechanism, disease association, protein domain, and molecular interaction. It unifies sequence, structure, and phenotypic inputs through multimodal co-training and instruction tuning, enabling zero-shot transfer and free-form phenotype generation. Evaluated on dozens of tasks, ProCyon outperforms specialized and generalist models in contextual retrieval, binding prediction, and variant effect inference. It generates candidate phenotypes for under-characterized proteins and supports discovery in complex diseases like Parkinson's. TxAgent is an AI agent for therapeutic reasoning that integrates real-time biomedical knowledge with a curated universe of 211 computational tools, including all FDA-approved drugs since 1939 and validated clinical evidence. TxAgent performs multi-step inference to assess drug interactions, contraindications, and patient-specific treatment strategies. It dynamically selects and executes tools, synthesizes information from multiple sources, and aligns recommendations with clinical guidelines. Across 3,168 drug reasoning tasks and 456 patient scenarios, TxAgent achieves 92.1% accuracy, outperforming specialized LLMs and reasoning agents (DeepSeek-R1-671B). ProCyon and TxAgent pave the way toward "AI scientists" systems that contribute to scientific understanding and therapeutic design and can eventually learn and innovate on their own.