Speaker: Lillian Chong, University of Pittsburgh

In this age of machine learning, a key frontier in molecular biophysics is moving beyond protein sequence and structure to uncover function—specifically, how biological processes proceed through the pathways connecting initial and target states. Weighted ensemble (WE) path sampling offers a powerful, physics-based approach for simulating these pathways, capturing rare events that occur on timescales as slow as seconds while maintaining rigorous kinetics. WE also enables efficient generation of conformational ensembles, revealing hidden functional states that often elude traditional structure determination techniques. In this talk, I will present recent advances in both methods and open-source software, and highlight how WE approaches and machine learning can serve complementary roles in the study of protein function.