Chemistry seminar with Dr. Andrew Ferguson from the University of Chicago at 4pm

Abstract: Data-driven modeling and deep learning present powerful tools that are opening up new paradigms and opportunities in the understanding, discovery, and design of soft and biological materials. In the first part of this talk, I will describe approaches based on conditional flow matching and adjoint matching to restore all-atom resolution to coarse-grained molecular dynamics simulations of proteins with state-of-the-art performance in reproducing structural ensembles and energy distributions. In the second part of the talk, I will discuss our recent work on autoregressive discrete diffusion models employing physicochemical and natural language text-prompting for data-driven functional protein design within experimental machine learning-guided directed evolution campaigns.