Machine Learning in Structural Biology

Workshop at the 37th Conference on Neural Information Processing Systems

December 2023


Structural biology, the study of the 3D structure or shape of proteins and other biomolecules, has been transformed by breakthroughs from machine learning algorithms. Machine learning models are now routinely used by experimentalists to predict structures to aid in hypothesis generation and experimental design, accelerate the experimental process of structure determination (e.g. computer vision algorithms for cryo-electron microscopy), and have become a new industry standard for bioengineering new protein therapeutics (e.g. large language models for protein design). Despite all of this progress, there are still many active and open challenges for the field, such as modeling protein dynamics, predicting the structure of other classes of biomolecules such as RNA, learning and generalizing the underlying physics driving protein folding, and relating the structure of isolated proteins to the in vivo and contextual nature of their underlying function. These challenges are diverse and interdisciplinary, motivating new kinds of machine learning methods and requiring the development and maturation of standard benchmarks and datasets.

Machine Learning in Structural Biology (MLSB), seeks to bring together field experts, practitioners, and students from across academia, industry research groups, and pharmaceutical companies to focus on these new challenges and opportunities. This year, MLSB aims to bridge the theoretical and practical by addressing the outstanding computational and experimental problems at the forefront of our field. The intersection of artificial intelligence and structural biology promises to unlock new scientific discoveries and develop powerful design tools.

MLSB will be an in-person workshop on December 15th at NeurIPS.

Please contact the organizers at with any questions.

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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
  • Design of or generative models for structure and/or sequence
  • Methods for structure determination / biophysics (Cryo-EM/ET, NMR, crystallography, single-molecule methods, etc.)
  • Geometric and symmetry-aware deep learning
  • Conformational change, ensembles, and dynamics
  • Integration of biomolecular physics
  • Function and property prediction
  • Structural bioinformatics and systems biology
  • Language models and other implicit representations of protein structure
  • Forward-looking position papers

We request anonymized PDF submissions by Thursday, October 5th, 2023, at 11:59PM, anywhere on earth through our submission 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 5 pages (excluding references and appendices) in PDF format, using the NeurIPS style files, and fully anonymized as per the requirements of NeurIPS. The NeurIPS checklist can be omitted from the submission. 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 workshop, with nominations of spotlight talks at the discretion of the organizers.

New this year, authors can opt-in for consideration for their submission to be included in a Special Collection in PRX Life (, a Physical Review journal publishing quantitative biology research, guest-edited by the MLSB organizers. Accepted papers in the MLSB Special Collection will have waived publication and open access fees. Stay tuned for more information.

Also new this year, authors that commit to open-sourcing code, model weights, and datasets used in the work will be given precedence for spotlight talks. This change only affects consideration for spotlights. Submissions that cannot make this commitment will still be considered for posters and will not be penalized for acceptance.

This workshop is considered non-archival, 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. We welcome papers sharing encouraging work-in-progress results or forward-looking position papers that would benefit from feedback and community discussion at our workshop.

Important Dates

Submission Deadline: Thursday, October 5th, 2023, at 11:59PM, Anywhere on Earth.

Notification of Acceptance: Thursday, October 27th, 2023.

Workshop Date: Friday, December 15th, 2023.

Invited Speakers

Bridget Carragher

Bridget Carragher

Founding Technical Director of the Chan-Zuckerberg Imaging Institute.

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Kyunghyun Cho

Kyunghyun Cho

Associate Professor at NYU
Senior Director of Frontier Research at Prescient Design.

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Rhiju Das

Rhiju Das

HHMI Investigator, Associate Professor of Biochemistry at Stanford University.

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Polly Fordyce

Polly Fordyce

Associate Professor of Genetics and
Bioengineering at Stanford University.

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Tanja Kortemme

Tanja Kortemme

Professor of Bioengineering at University of California, San Francisco.

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RF Diffusion Team

A diffusion model for protein design.

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Photo of Gabriele Corso

Gabriele Corso

Photo of Gina El Nesr

Gina El Nesr
Stanford University

Photo of Sergey Ovchinnikov

Sergey Ovchinnikov
Harvard University

Photo of Roshan Rao

Roshan Rao
Meta AI

Photo of Hannah Wayment-Steele

Hannah Wayment-Steele
Brandeis University

Photo of Ellen Zhong

Ellen Zhong
Princeton University