Meizhong Jin
Seminars
As molecular glue discovery expands beyond traditional cereblon biology, researchers
face a critical challenge: predicting which induced-proximity events will translate into
meaningful biological outcomes. Ternary complex formation, cooperativity, protein
dynamics, and downstream functionality remain difficult to forecast, often forcing
teams to rely on resource-intensive experimental testing. To address this challenge,
AstraZeneca has developed mathematical and computational approaches to better
understand molecular glue behaviour, prioritize discovery opportunities, and guide
experimental strategy. This workshop will explore how quantitative modelling can be
integrated with structural biology, proteomics, and experimental data to improve hit
selection, reduce discovery risk, and accelerate the path toward higher-confidence glue
programmes.
Join this workshop to:
- Discover how mathematical models can be used to predict ternary complex formation, cooperativity, and functional molecular glue activity, enabling more informed compound and target prioritization decisions
- Understand how quantitative frameworks can address major discovery bottlenecks, including protein dynamics, conformational change, limited datasets, and the challenge of identifying productive induced-proximity events
- Learn how to combine computational predictions with structural biology, proteomics, and experimental validation to create an end-to-end workflow that reduces discovery risk and improves programme success
