Machine Learning in Structural Biology

Workshop at the 36th Conference on Neural Information Processing Systems

Saturday, December 3rd, 2022

About

In only a few years, 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 being used by experimentalists to predict structures that can help answer real biological questions (e.g. AlphaFold), 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 this progress, there are still many active and open challenges for the field, such as modeling protein dynamics, predicting higher order complexes, pushing towards generalization of protein folding physics, and relating the structure of proteins to the in vivo and contextual nature of their underlying function. These challenges are diverse and interdisciplinary, motivating new kinds of machine learning systems and requiring the development and maturation of standard benchmarks and datasets.

In this exciting time for the field, our workshop, “Machine Learning in Structural Biology” (MLSB), seeks to bring together relevant experts, practitioners, and students across a broad community to focus on these challenges and opportunities. We believe the union of these communities, including the geometric and graph learning communities, NLP researchers, and structural biologists with domain expertise at our workshop can help spur new ideas, spark collaborations, and advance the impact of machine learning in structural biology. Progress at this intersection promises to unlock new scientific discoveries and the ability to design novel medicines.

MLSB will be an in-person/hybrid workshop on December 3rd, 2022 at NeurIPS.

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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
  • Generative models for structure and/or sequence
  • Protein, RNA, and DNA design
  • Protein structure determination including cryo-EM/ET
  • 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 representations of proteins
  • Forward-looking position papers

We request anonymized PDF submissions by Thursday, September 29th, 2022, at 11:59PM anywhere on earth 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. 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. 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, September 29th, 2022, 11:59 PM, Anywhere on Earth.

Notification of Acceptance: Friday, October 20th, 2022.

Workshop Date: Saturday, December 3rd, 2022.

Invited Speakers

David Fleet

David Fleet

Professor of Computer Science at University of Toronto.

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Alexander Rives

Alexander Rives

Research Scientist at Meta AI.

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Kathryn Tunyasuvunakool

Kathryn Tunyasuvunakool

Staff Research Scientist at DeepMind.

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Max Welling

Max Welling

Distinguished Scientist at Microsoft Research.

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Schedule (EST)

08:30Opening Remarks
08:35Invited Speaker - David Fleet
08:40
08:45
08:50
08:55
09:00Contributed Talk

Latent Space Diffusion Models of Cryo-EM Structures

09:05
09:10
09:15Contributed Talk

Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models

09:20
09:25
09:30Contributed Talk

Predicting conformational landscapes of known and putative fold-switching proteins using AlphaFold2

09:35
09:40
09:45Break
09:50
09:55
10:00
10:05Invited Speaker - Kathryn Tunyasuvunakool
10:10
10:15
10:20
10:25
10:30Contributed Talk

SWAMPNN: End-to-end protein structures alignment

10:35
10:40
10:45Contributed Talk

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

10:50
10:55
11:00Contributed Talk

Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models

11:05
11:10
11:15Poster Session
11:20
11:25
11:30
11:35
11:40
11:45
11:50
11:55
12:00
12:05
12:10
12:15Lunch
12:20
12:25
12:30
12:35
12:40
12:45
12:50
12:55
01:00Invited Speaker - Max Welling
01:05
01:10
01:15
01:20
01:25Contributed Talk

EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation

01:30
01:35
01:40Contributed Talk

Predicting Ligand – RNA Binding Using E3-Equivariant Network and Pretraining

01:45
01:50
01:55Invited Speaker - Alexander Rives
02:00
02:05
02:10
02:15
02:20Contributed Talk

Seq2MSA: A Language Model for Protein Sequence Diversification

02:25
02:30
02:35Contributed Talk

Metal3D: Accurate prediction of transition metal ion location via deep learning

02:40
02:45
02:50Panel Discussion
02:55
03:00
03:05
03:10
03:15
03:20
03:25
03:30
03:35
03:40
03:45
03:50Poster Session / Happy Hour
03:55
04:00
04:05
04:10
04:15
04:20
04:25
04:30
04:35
04:40
04:45
04:50
04:55Closing Remarks

Organizers

Jonas
Adler

Photo of Jonas Adler
DeepMind

Namrata
Anand

Photo of Namrata Anand
Stanford

John
Ingraham

Photo of John Ingraham
Generate Biomedicines

Sergey
Ovchinnikov

Photo of Sergey Ovchinnikov
Harvard University

Roshan
Rao

Photo of Roshan Rao
Meta AI

Ellen
Zhong

Photo of Ellen Zhong
Princeton