Generative and Agentic AI for Biology

The 2024 Nobel Prize in Chemistry, awarded for AI-based protein structure prediction and protein design, underscored the transformative impact of machine learning on the life sciences. Generative AI models, including large language models, diffusion models, and foundation models for biological sequences and cells, have demonstrated remarkable success in modeling and designing biomolecules and biological systems. However, a new paradigm is emerging. Beyond generating biological sequences or structures, AI systems are beginning to act as agents: formulating hypotheses, planning experiments, interacting with tools and databases, and iteratively refining scientific strategies. This workshop aims to explore the future of AI for biology at the intersection of these two paradigms. Rather than focusing solely on incremental advances in generative modeling, we seek to engage the community in a deeper discussion about the conceptual and practical foundations of AI-driven biological discovery. Key questions include: * Will agentic AI subsume generative models, or are they complementary components of future scientific systems? * In what biological problems is agentic AI necessary? * What architectures are required for AI systems that reason across molecules, cells, tissues, and organisms? * How should we evaluate AI agents that participate in biological discovery? * What is the role of human scientists in an era of AI-driven hypothesis generation and experimentation? We aim to discuss these questions through invited talks, poster presentations, and panel discussions on the following topics: * Generative models for biomolecule and therapeutic design. * Agent-based systems for hypothesis generation, experimental planning, and closed-loop wet-lab integration. * Foundation models and world models for multi-scale biology. * Benchmarks and evaluation frameworks for autonomous scientific systems. * Human-AI collaboration paradigms in biological research. * Safety, governance, and ethical considerations of autonomous biological AI systems.