In a unique plenary series of intimate conversations, we will explore the models, drivers, and challenges facing rare disease research. By uniting leaders in precision medicine, bioinformatics, national rare-disease infrastructure, and real-world legislative advocacy, we will give attendees an expansive, cross-disciplinary view of what’s required to deliver faster, more accurate, and more equitable rare-disease cures.

The pharmaceutical industry sits on a collective treasure trove of proprietary structural biology data, yet competitive concerns have historically prevented the data sharing necessary to train the most powerful AI models for drug discovery. Federated learning is changing this paradigm, enabling biopharma companies to collaborate on AI model training while keeping sensitive data secure and confidential. This plenary session explores the groundbreaking AI Structural Biology (AISB) Network, where industry leaders are pooling proprietary protein-ligand structure data to collaboratively train OpenFold3, an AI model designed to predict molecular interactions with precision approaching X-ray crystallography. Through the federated computing platform, thousands of experimentally determined protein–small molecule structures remain securely at their original locations while contributing to a shared learning framework that no single organization could achieve alone. This session reveals how federated learning solves the industry's most persistent challenge: unlocking collective intelligence while protecting intellectual property. ​Attendees will hear directly from consortium leaders about: 

• The technical architecture enabling privacy-preserving collaborative AI training across competing organizations 
• Real-world implementation of federated learning platforms and computational governance frameworks 
• Strategic rationale for industry collaboration: why sharing model training beats going it alone 
• Impact and outcomes from early OpenFold3 results in predicting binding affinities and accelerating small molecule discovery 
• The future of collaborative AI in biopharma, from structural biology to clinical development

8:05 AM

Bio-IT World 2026 Innovative Practices Awards Ceremony (Winners Announced)

The Innovative Practices Awards recognizes and celebrates technology innovation in the life sciences. Bio-IT World is currently accepting entries for the 2026 Innovative Practices Awards, a competition designed to recognize partnerships and projects pushing our industry forward. Winners will be announced in April 2026, recognized during the Thursday May 21 Plenary Keynote Program, and scheduled to give a podium presentation about their project during the conference. The deadline for entry is March 2, 2026. For more details about the Awards and to submit an application, visit www.bioitworldexpo.com/innovativepractices.

8:20 AM

Bio-IT World 2026 Emerging Innovator Award—NEW (Winner Announced)

The Emerging Innovator Award recognizes one exceptional early-career researcher advancing the future of life sciences through breakthrough work in biomedical data, computational methods, or technology-enabled discovery. The 2026 awardee will deliver a 10-minute plenary keynote at Bio-IT World, highlighting the impact of their research and the forward-looking direction of their work. Nominations are due March 2, 2026, at www.bio-itworldexpo.com.

Many steps in drug discovery are informed by large-scale biological and chemical data, from genomics to the chemical universe, to phenotypic cell profiling. Generative-ML models are increasingly being deployed across these domains, including single-cell foundation models for target discovery, generative chemistry for rapid ligand design, and transfer learning to accelerate image analysis. Practical applications of generative AI in early drug discovery will be described, including simulated functional-genomics screens with in silico perturbations; compound design conditioned on protein pockets; and in silico-labeling approaches that replace traditional image staining. Together, these advances illustrate how generative AI is transforming how drug discovery research is conducted.