Machine Learning-based Design of Proteins and Small Molecules

Explore the cutting-edge intersection of machine learning and protein engineering in this research seminar from the University of California, Berkeley. Delve into the world of data-driven design as applied to proteins, small molecules, and materials engineering. Learn how high-capacity regression models trained on labeled data are revolutionizing the search for promising design candidates, potentially surpassing the best designs in observed data. Examine the unique challenges of machine learning-based design, which requires extrapolation into unknown parts of the design space. Gain insights into emerging computational approaches tackling these challenges, with a focus on protein engineering applications. Presented by Jennifer Listgarten, a professor in UC Berkeley's Department of Electrical Engineering and Computer Science and a member of the Berkeley AI Research Lab, this seminar offers a comprehensive look at the future of computational biology and machine learning-driven design.