Dr. Ivan Kryukov is a Senior Genomic Data Scientist at Bayer, where he specializes in target discovery and validation within cardiovascular and renal therapeutic areas. Among others, he represents Bayer on the Alliance for Genomic Discovery, driving collaborative research that leverages large-scale biobank data at the intersection of genomic research and EHR. His current work focuses on the application of large-scale AI to medical data to accelerate drug target identification and validation. Dr. Kryukov received his doctorate in population genetics from the University of Calgary and completed postdoctoral research at McGill University, Canada.
Actionability of data in drug discovery depends on the completeness of underlying datasets and the analytical infrastructure to generate meaningful insights. This is amplified in the era of AI-driven interpretation, where models are only as powerful as the data they're trained on. As drug discovery teams adopt rapidly advancing approaches like machine learning for target identification, virtual cell modeling, and multiomic profiling, access to large-scale, diverse datasets with complete metadata has become a strategic imperative. This panel examines how hyperscale initiatives like the Alliance for Genomic Discovery (350,000+ whole genomes and 50,000+ linked proteomes) and the Illumina Billion Cell Atlas are providing the foundational data infrastructure for next-generation drug discovery. Panelists will bring expertise spanning functional genomics, machine learning, ADME, antibody developability and more to discuss what it takes to build AI-ready datasets, the importance and challenges of integrating across diverse datasets and infrastructures, and how both proprietary and pre-competitive collaboration are impacting today’s R&D landscape.