Kejia Wu
Post Doc, Baker Lab, Institute for Protein Design Washington University School of Medicine in St. Louis
Kejia Wu is a Postdoctoral Researcher in the Baker Lab at the Institute for Protein Design, University of Washington, where she develops methods to design and characterize binders for peptides and intrinsically disordered regions. Her work focuses on capturing transient, weak, and highly dynamic interactions, and on functionalizing designed binders to probe complex biological mechanisms. Kejia has contributed to high-impact publications advancing binder design for disordered targets and expanding the toolkit for studying structural ensembles. At the Summit, she will discuss biophysical strategies for characterizing IDPs and interpreting data when no single static structure exists.
Seminars
Artificial intelligence and machine learning are transforming the way biophysical data is generated, interpreted, and applied. But beyond the day-to-day integration of AI into experimental workflows, what are the broader opportunities, challenges, and limitations?
This interactive panel brings together leading experimentalists, computational biophysicists, and AI specialists to explore the bigger picture and provides you the opportunity to have honest conversations with your industry colleagues about hype vs. value.
Key Discussion Themes:
- Where can AI add true value in biophysics beyond data processing? Predictive modelling, hit triaging, protein design?
- Which areas of drug discovery are most ready for AI adoption and where should caution succeed?
- The challenges of AI adoption including data curation, standardization, reproducibility, interpretability, and integration with lab workflows
- The evolving role of experimental validation alongside AI predictions, how to ensure we maintain scientific rigor
Blue Sky Thinking & Audience Feedback: In 5 years, what do you hope AI/ML has enabled / transformed the way you do something?
- Explore the fundamental challenges of studying proteins that lack a fixed 3D structure and how conformational heterogeneity and how it complicates affinity determination and stoichiometry
- Discuss approaches like NMR, HDX-MS, SAXS, smFRET, and cryo-EM for resolving structural ensembles and learn which is best suited to detect weak and transient interactions
- Learn strategies to interpret biophysical data when no single static structure exists
