MLFPM Summer School 2021: Jennifer Listgarten
Channel: MLFPM ITN
Date: 2021-11-30

Third Summer School "Machine Learning Frontiers in Precision Medicine", September 20-22, 2021

Jennifer Listgarten (University of California, Berkeley): Machine learning-based design of proteins (and small molecules and beyond)

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 that binds to a target more tightly than previously observed. 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 computational approaches to tackle it.

The summer school is part of the H2020 Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (https://mlfpm.eu). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 813533.