"Machine learning in chemistry and beyond" (ChE-605) seminar by Dr. Wenhao Gao: Navigating synthesizable chemical space with generative AI
Event details
| Date | 28.10.2025 |
| Hour | 17:00 › 18:00 |
| Speaker | Wenhao Gao is an incoming Assistant Professor in Chemical and Biomolecular Engineering at the University of Pennsylvania. He is currently a postdoctoral researcher at Stanford University with Prof. Grant Rotskoff and Stefano Ermon. Wenhao received his Ph.D. from MIT, where he was advised by Prof. Connor W. Coley. His research focuses on developing artificial intelligence methods that integrate chemical and physical principles to enable systematic and scalable molecular discovery for applications in drug design and sustainable materials. He has been recognized with numerous honors, including the Google PhD Fellowship, Takeda Fellowship, D. E. Shaw Research Fellowship, CAS Future Leaders recognition, and Forbes 30 Under 30 Asia in Healthcare and Science. |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
The discovery of functional molecules plays a fundamental role in advancing chemical science and engineering, yet it remains a costly and time-intensive process. Recent advances in computational methods, particularly in generative artificial intelligence, have introduced a new approach, generative molecular design, which holds the promise of efficiently identifying molecules with desired properties. However, despite significant progress, their practical impact in real-world applications has been limited. In this talk, I will present our efforts to address critical bottlenecks in generative molecular design, namely synthetic accessibility and sample efficiency. I will present the development of benchmarks that capture real-world complexity and the development of chemistry-tailored solutions to enhance the practicality of generative algorithms. Taken together, these advances aim to close the gap between computational innovation and practical feasibility, paving the way for the accelerated, AI-driven discovery of novel functional molecules.
Practical information
- General public
- Free
Organizer
- Victor Sabanza Gil, Edvin Fako, Philippe Schwaller