Yu-Hua Lo
Director of CryoEM Science & Operations Eli Lilly & Co.
Yu-Hua Lo is Director of CryoEM Science and Operations at Eli Lilly and Company, where she leads cryo-electron microscopy capabilities supporting drug discovery and structural biology initiatives. Prior to her current role, she served as Senior Research Scientist in Lilly’s CryoEM group, advancing the application of high-resolution structural biology to therapeutic development. Before joining Lilly, Yu-Hua was a CryoEM Scientist at St. Jude Children’s Research Hospital and held research positions at the National Institute of Environmental Health Sciences (NIEHS) and KEK in Japan. Her expertise spans cryo-EM, structural biology, protein characterization, and the integration of advanced imaging technologies into biomedical research.
Seminars
- Reducing manual intervention across sample preparation, data collection and structure determination to improve efficiency and reproducibility
- Identifying key workflow limitations and implementing scalable approaches to increase cryo-EM throughput and accelerate structure generation
- Leveraging optimized workflows and facility capabilities to deliver structural insights faster and support more efficient drug discovery decisions
As cryo-EM becomes increasingly important for structure-based drug discovery, the field is facing a fundamental throughput challenge. Long data collection times, limited microscope access, difficult sample preparation, computationally intensive analysis, and manual structure building can restrict the number of structures that can be delivered to discovery teams. Join structural biology leaders, cryo-EM specialists, and technology developers as they discuss how to scale cryo-EM workflows to meet the growing demands of modern drug discovery by:
- Overcoming access and throughput limitations, examining how companies are managing constrained microscope capacity, external facilities, CRO access, and the need to generate sufficient structures to support increasingly large discovery pipelines
- Improving sample preparation and data quality, discussing how grid development, alternative sample preparation approaches, additives, and better understanding of sample-specific behaviour can reduce failed experiments and increase the probability of obtaining high-quality structures
- Automating the cryo-EM workflow, exploring how advances in instrumentation, AI-enabled data processing, automated structure building, and improved computational infrastructure can reduce manual intervention and increase the number of actionable structures delivered to drug discovery teams
