Machine Learning for Structural Biology

Workshop at the 35th Conference on Neural Information Processing Systems

Monday, December 13, 2021


Structural biology, the study of proteins and other biomolecules through their 3D structures, is a field on the cusp of transformation. While measuring and interpreting biomolecular structures has traditionally been an expensive and difficult endeavor, recent machine-learning based modeling approaches have shown that it will become routine to predict and reason about structure at proteome scales with unprecedented atomic resolution. This broad liberation of 3D structure within bioscience and biomedicine will likely have transformative impacts on our ability to create effective medicines, to understand and engineer biology, and to design new molecular materials and machinery. Machine learning also shows great promise to continue to revolutionize many core technical problems in structural biology, including protein design, modelling protein dynamics, predicting higher order complexes, and integrating learning with experimental structure determination.

At this inflection point, we hope that the Machine Learning in Structural Biology (MLSB) workshop will help bring community and direction to this rising field. To achieve these goals, this workshop will bring together researchers from a unique and diverse set of domains, including core machine learning, computational biology, experimental structural biology, geometric deep learning, and natural language processing.

Call For Papers

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

  • Prediction of biomolecular structures, complexes, and interactions
  • Geometric and symmetry-aware deep learning
  • Protein and RNA design
  • Generative models for structure and sequence
  • Experimental structure determination
  • Conformational change, ensembles, and dynamics
  • Function and property prediction
  • Structural systems biology
  • Language models and other representations of proteins

We request anonymized PDF submissions by Friday, September 17th Friday, October 1st, 2021, 11:59PM in the timezone of your choice through our submission website on CMT.

Papers should present novel work that has not been previously accepted at an archival venue at the time of submission. Submissions should be a maximum of 4 pages (excluding references and appendices) in PDF format and fully anonymized as per the requirements of NeurIPS. We request use of the NeurIPS style files. Submissions meeting these criteria will go through a light, double-blind review process. Reviewer comments will be returned to the authors as feedback.

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: Friday, September 17th Friday, October 1st, 2021, 11:59 PM, Anywhere on Earth.

Notification of Acceptance: Friday, October 22nd, 2021.

Workshop Date: Monday, December 13th, 2021.

Invited Speakers

John Jumper

John Jumper


Senior Staff Research Scientist at DeepMind.

Jane Richardson

Jane Richardson


Professor of Biochemistry at Duke University.

Regina Barzilay

Regina Barzilay

Professor of Computer Science at Massachusetts Institute of Technology.

Michael Bronstein

Michael Bronstein

Professor of Machine Learning at Imperial College London.

Cecilia Clementi

Cecilia Clementi

Professor of Computational Biophysics at Freie Universit├Ąt Berlin.

Lucy Colwell

Lucy Colwell

Assistant Professor of Chemistry at University of Cambridge and Research Scientist at Google.

Amy Keating

Amy Keating

Professor of Biology at Massachusetts Institute of Technology.

Derek Lowe

Derek Lowe

Director in Chemical Biology at Novartis Institutes for BioMedical Research.


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