Meizhong Jin

Senior Director of Oncology Chemistry AstraZeneca

Meizhong Jin is Senior Director of Oncology Chemistry at AstraZeneca, where he leads drug discovery efforts across oncology and targeted protein degradation. With more than 15 years of industry experience, he has advanced multiple clinical candidates and held leadership roles at Arrakis Therapeutics, Warp Drive Bio, Enanta Pharmaceuticals, and Arvinas. His contributions include the discovery of first-in-class therapeutics, including androgen receptor degraders and molecular glue KRAS inhibitors.

Seminars

Tuesday 26th January 2027
Predicting Productive Molecular Glues: Leveraging Mathematical Modelling to Improve Discovery Success
9:00 am - 12:00 pm

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
Meizhong Jin