Daniel Dai
Scientific Manager Sanofi
Daniel Dai is a Scientific Manager in Translational Proteomics at Sanofi, where he leverages proteomics, computational biology, and AI-driven approaches to advance drug discovery and development. His expertise spans target identification, biomarker discovery, mechanism-of-action studies, and multi-omics data integration to support therapeutic innovation. Prior to joining Sanofi, Daniel conducted research at Northeastern University’s Barnett Institute, developing novel mass spectrometry technologies for protein characterization and quantitative proteomics. He holds a PhD in Analytical Chemistry from the Beijing Institute of Radiation Medicine and has built a distinguished career applying translational proteomics and systems biology to accelerate the development of new medicines.
Seminars
Analysis of multi-omics data sets represents a powerful tool to understand human biological and functional context for novel target discovery and mechanistic insights for therapeutic development.
Join this workshop to delve into best approaches to access and standardize multiomics datasets and leverage AI/ML platforms to drive biologically meaningful target and biomarker identification to increase the rate of clinical success from discovery.
Workshop Highlights Include:
- How much data is enough? Understanding best approaches to access and combine proprietary and public datasets to train AI/ML computational multi-omics platforms
- Undergoing multi-omics analysis to drive pathway and target discovery, highlighting the significance of biological validation to improve likelihood of success in the clinic
- Understanding applications of AI/ML models to leverage multi-omics data and functional context to identify biomarkers for patient responders and non-responders
- Contextualizing pharma’s attitude to “lock and key” drug discovery hurdles, and how to best perform biological validation of target protein over designing better drugs
- Applying AI tools across multi-omics data and alternative experimental design to investigate the causal impact of drugging target proteins
- Assessing the reliability of AI within target protein validation to maximize the success rate of drugs in the clinic at the discovery stage
