The next C3.ai Digital Transformation Institute Colloquium on Digital Transformation Science webinar will be Thursday, October 22 at 3:00 pm U.S. Central Time. Presenting "Machine Learning-based Design of Proteins, Small Molecules, and Beyond" will be Jennifer Listgarten, Professor, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley.
Registration is required to attend this webinar.
Abstract: Data-driven design is making headway into a number of application areas, including protein, small-molecule, and materials engineering. The design goal is to construct an object with desired properties, such as a protein therapeutic that binds tightly to its target. To that end, costly experimental measurements are being replaced with calls to a high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for promising design candidates. The aim then is to discover designs that are better than the best design in the observed data. This goal puts machine learning-based design in a much more difficult spot than traditional applications of predictive modelling, since successful design requires, by definition, some degree of extrapolation—a pushing of the predictive models to its unknown limits, in parts of the design space that are a priori unknown. In this talk, I will anchor this overall problem in protein engineering and discuss our emerging approaches to tackle it.
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