Abstract

The integration of generative AI into drug discovery is moving beyond simple structure prediction toward a more comprehensive and autonomous pipeline. In this talk, I will focus on our recent efforts to accelerate AI-driven drug discovery (AIDD) through a multi-layered approach. I will first present our work on de novo protein and peptide sequencing, which enables the high-resolution data acquisition necessary for identifying novel targets. I will then delve into our core research on biomolecular structure prediction, discussing how we optimize these models for the specific challenges of therapeutic design. Finally, I will briefly explore how these generative tools are setting the stage for agentic science, where autonomous systems begin to orchestrate complex discovery workflows.

About the speaker

Siqi Sun is an associate professor at Fudan University and a researcher at the Shanghai AI Lab. He previously served as a researcher at Microsoft Research, Redmond. He holds a PhD from the Toyota Technological Institute at Chicago (TTIC) and a bachelor's degree in Mathematics from Fudan University. His research focuses on AI for science, specifically developing generative models and standardized benchmarks for proteomics and structural biology.