(443c) Data-Driven Protein Design Using Autoregressive Discrete Diffusion Models

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. I will describe our recent work on autoregressive discrete diffusion models employing physicochemical and natural language conditioning for data-driven functional protein design within machine learning-guided directed evolution campaigns.