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Machine Learning for Structural Biology
Raphael Townshend · Stephan Eismann · Ron Dror · Ellen Zhong · Namrata Anand · John Ingraham · Wouter Boomsma · Sergey Ovchinnikov · Roshan Rao · Per Greisen · Rachel Kolodny · Bonnie Berger

Sat Dec 12 08:00 AM -- 06:00 PM (PST) @
Event URL: http://mlsb.io »

Spurred on by recent advances in neural modeling and wet-lab methods, structural biology, the study of the three-dimensional (3D) atomic structure of proteins and other macromolecules, has emerged as an area of great promise for machine learning. The shape of macromolecules is intrinsically linked to their biological function (e.g., much like the shape of a bike is critical to its transportation purposes), and thus machine learning algorithms that can better predict and reason about these shapes promise to unlock new scientific discoveries in human health as well as increase our ability to design novel medicines.

Moreover, fundamental challenges in structural biology motivate the development of new learning systems that can more effectively capture physical inductive biases, respect natural symmetries, and generalize across atomic systems of varying sizes and granularities. Through the Machine Learning in Structural Biology workshop, we aim to include a diverse range of participants and spark a conversation on the required representations and learning algorithms for atomic systems, as well as dive deeply into how to integrate these with novel wet-lab capabilities.

Author Information

Raphael Townshend (Stanford University)
Stephan Eismann (Stanford University)
Ron Dror (Stanford)
Ellen Zhong (Massachusetts Institute of Technology)
Namrata Anand (Stanford University)
John Ingraham (Generate Biomedicines)
Wouter Boomsma (University of Copenhagen)
Sergey Ovchinnikov (Harvard)
Roshan Rao (UC Berkeley)
Per Greisen (Novo Nordisk)
Rachel Kolodny (Haifa University)
Bonnie Berger (MIT)

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