BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Jennifer Listgarten (UC Berkeley) DTSTART:20200707T163000Z DTEND:20200707T174500Z DTSTAMP:20260423T003241Z UID:IASML/9 DESCRIPTION:Title: Ma chine learning-based design (of proteins\, small molecules and beyond) \nby Jennifer Listgarten (UC Berkeley) as part of IAS Seminar Series on Th eoretical Machine Learning\n\n\nAbstract\nData-driven design is making hea dway into a number of application areas\, including protein\, small-molecu le\, and materials engineering. The design goal is to construct an object with desired properties\, such as a protein that binds to a target more ti ghtly than previously observed. To that end\, costly experimental measurem ents are being replaced with calls to a high-capacity regression model tra ined on labeled data\, which can be leveraged in an in silico search for p romising design candidates. The aim then is to discover designs that are b etter than the best design in the observed data. This goal puts machine-le arning based design in a much more difficult spot than traditional applica tions of predictive modelling\, since successful design requires\, by defi nition\, some degree of extrapolation---a pushing of the predictive models to its unknown limits\, in parts of the design space that are a priori un known. In this talk\, I will anchor this overall problem in protein engine ering\, and discuss our emerging approaches to tackle it.\n LOCATION:https://researchseminars.org/talk/IASML/9/ END:VEVENT END:VCALENDAR