Machine Learning for Structural Biology

Workshop at the 34th Conference on Neural Information Processing Systems

About

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.

Call For Papers

We welcome submissions of short papers leveraging machine learning to address problems in structural biology, including but not limited to:

  • Structure prediction
  • Protein and RNA design
  • Experimental determination of structure
  • Interaction prediction
  • Conformational change and ensemble prediction
  • Molecular dynamics with learned samplers or potential functions
  • Function or property prediction
  • Structural systems biology
  • Model systems, such as lattice proteins or other toy ensembles
  • Learning representations of structure

We request anonymized PDF submissions by Thursday, October 8th, 2020, 11:59PM in the timezone of your choice through Microsoft CMT.

Submissions should be 4-9 pages in PDF format and fully anonymized as per the requirements of NeurIPS. We request use of the NeurIPS style files. A maximum of 9 pages excluding references and appendices will be considered. The review process will be double-blind.

Accepted papers will be invited to present a poster at the virtual workshop, with nominations of spotlight talks at the discretion of the organizers. This workshop is considered non-archival and does not publish proceedings, however authors of accepted contributions will have the option to make their work available through the workshop website. Presentation of work that is concurrently in submission is welcome.

Important Dates

Submission Deadline: October 02, 2020 October 08, 2020, 11:59 PM, Anywhere on Earth.

Notification of Acceptance: October 23, 2020

Workshop Date: December 11th or 12th, 2020

Invited Speakers

David Baker

David Baker

Keynote

Director of the Institute for Protein Design, University of Washington.

Michael Levitt

Michael Levitt

Keynote

Professor of Structural Biology at Stanford University. Recipient of the 2013 Nobel Prize in Chemistry.

Mohammed AlQuraishi

Mohammed AlQuraishi

Assistant Professor of Systems Biology at Columbia University.

Charlotte Deane

Charlotte Deane

Professor of Structural Bioinformatics at Oxford University.

Debora Marks

Debora Marks

Associate Professor of Systems Biology at Harvard Medical School.

Frank Noe

Frank Noe

Professor of Mathematics and Computer Science at Freie Universität Berlin.

Chaok Seok

Chaok Seok

Professor of Chemistry at Seoul National University.

Andrea Thorn

Andrea Thorn

Group leader at the Rudolf Virchow Center of Würzburg University.

Pre-Registration

Organizers

Raphael
Townshend

Stanford University

Stephan
Eismann

Stanford University

Ron
Dror

Stanford University

Ellen
Zhong

MIT

Namrata
Anand

Stanford University

John
Ingraham

Generate Biomedicines

Wouter
Boomsma

University of Copenhagen

Sergey
Ovchinnikov

Harvard University

Roshan
Rao

UC Berkeley

Per
Greisen

Novo Nordisk

Rachel
Kolodny

University of Haifa

Bonnie
Berger

MIT