Please note: This PhD defence will take place in DC 2314 and online.
Bing Hu, PhD candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Helen Chen & Anita Layton
Bringing a new drug to market typically requires $2-3 billion and 10-15 years, with the majority of candidates failing due to poor pharmacokinetic properties or insufficient efficacy. Artificial intelligence has the potential to dramatically accelerate pre-clinical drug discovery, but a fundamental obstacle stands in the way: sparse overlap between datasets. Drug discovery datasets are collected independently, covering different molecules and their properties with little overlap, making it impossible to train the kind of large, unified foundation models that have revolutionized natural language processing and computer vision. This thesis presents a systematic research approach to overcome data overlap sparsity through generative synthetic data, developing four interconnected models that progressively advance toward a generative foundation model for pre-clinical drug discovery.
You can attend this PhD defence through:
- Online: Microsoft Teams
- In person: DC 2314