Machine Learning for Structural Biology

Workshop at the 34th Conference on Neural Information Processing Systems

Saturday, December 12

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.

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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 Universit├Ąt Berlin.

Chaok Seok

Chaok Seok

Professor of Chemistry at Seoul National University.

Andrea Thorn

Andrea Thorn

Group leader at the Rudolf Virchow Center of W├╝rzburg University.

Schedule (PST)

08:00 Opening Remarks
08:10 Keynote - Michael Levitt
08:20
08:30
08:40
08:50 Invited Talk - Charlotte Deane
9:00
9:10 Invited Talk - Frank Noe
9:20
9:30 Invited Talk - Andrea Thorn
9:40
9:50 Break
10:00
10:10
10:20 Keynote - David Baker
10:30
10:40
10:50
11:00 Contributed Talk: Predicting Chemical Shifts with Graph Neural Networks (Ziyue Yang, Maghesree Chakraborty, Andrew White)
11:10 Contributed Talk: Cryo-ZSSR: multiple-image super-resolution based on deep internal learning (Qinwen Huang, Ye Zhou, Xiaochen Du, Reed Chen, Jianyou Wang, Cynthia Rudin, Alberto Bartesaghi)
11:20 Contributed Talk: Wasserstein K-Means for Clustering Tomographic Projections (Rohan Rao, Amit Moscovich, Amit Singer)
11:30 Poster Session
11:40
11:50
12:00
12:10
12:20
12:30 Lunch
12:40
12:50 Lunch
01:00
01:10
01:20
01:30
01:40
01:50
2:00 Invited Talk - Debora Marks
2:10
2:20 Contributed Talk: ProGen: Language Modeling for Protein Generation (Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Alexander E Chu, Raphael R Eguchi, Po-Ssu Huang, Richard Socher)
2:30 Contributed Talk: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Demi Guo, Myle Ott, Larry Zitnick, Jerry Ma, Rob Fergus)
2:40 Contributed Talk: SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning (Jonathan King, David Koes)
2:50 Contributed Talk: Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models (Tomohide Masuda, Matthew Ragoza, David Koes)
3:00 Contributed Talk: Learning from Protein Structure with Geometric Vector Perceptrons (Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael Townshend, Ron Dror)
3:10 Poster Session
3:20
3:30
3:40
3:50
4:00
4:10 Invited Talk - Mohammed AlQuraishi
4:20
4:30 Invited Talk - Chaok Seok
4:40
4:50 Closing Remarks

Accepted Papers

  • Predicting Chemical Shifts with Graph Neural Networks

    Ziyue Yang, Maghesree Chakraborty, Andrew White

  • DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization

    Azadeh Alavi, David Ascher

  • Cryo-ZSSR: multiple-image super-resolution based on deep internal learning

    Qinwen Huang, Ye Zhou, Xiaochen Du, Reed Chen, Jianyou Wang, Cynthia Rudin, Alberto Bartesaghi

  • Learning Super-Resolution Electron Density Map of Proteins using 3D U-Net

    BAISHALI MULLICK, Yuyang Wang, Prakarsh Yadav, Amir Barati Farimani

  • Wasserstein K-Means for Clustering Tomographic Projections

    Rohan Rao, Amit Moscovich, Amit Singer

  • Exploring generative atomic models in cryo-EM reconstruction

    Ellen Zhong, Adam Lerer, Joseph Davis, Bonnie Berger

  • Learning from Protein Structure with Geometric Vector Perceptrons

    Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael Townshend, Ron Dror

  • Protein model quality assessment using rotation-equivariant, hierarchical neural networks

    Stephan Eismann, Patricia Suriana, Bowen Jing, Raphael Townshend, Ron Dror

  • MXMNet: A Molecular Mechanics-Driven Neural Network Based on Multiplex Graph for Molecules

    Shuo Zhang, Yang Liu, Lei Xie

  • ProGen: Language Modeling for Protein Generation

    Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Alexander E Chu, Raphael R Eguchi, Po-Ssu Huang, Richard Socher

  • Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

    Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Demi Guo, Myle Ott, Larry Zitnick, Jerry Ma, Rob Fergus

  • ESM-1b: Optimizing Evolutionary Scale Modeling

    Joshua Meier, Jason Liu, Zeming Lin, Naman Goyal, Myle Ott, Tom Sercu, Alexander Rives

  • Is Transfer Learning Necessary for Protein Landscape Prediction?

    Amir Shanehsazzadeh, David Belanger, David Dohan

  • Fast and adaptive protein structure representations for machine learning

    Janani Durairaj, Mehmet Akdel, Dick de Ridder, Aalt DJ van Dijk

  • Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models

    Pascal Sturmfels, Jesse Vig, Ali Madani, Nazneen Fatema Rajani

  • Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks

    Modestas Filipavicius

  • The structure-fitness landscape of pairwise relations in generative sequence models

    Dylan Marshall, Haobo Wang, Michael Stiffler, Justas Dauparas, Peter K. Koo, Sergey Ovchinnikov

  • Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning

    Marcin J Skwark, Nicolas Lopez Carranza, Thomas Pierrot, Joe Phillips, Slim Said, Alexandre Laterre, Amine Kerkeni, Ugur Sahin, Karim Beguir

  • Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction

    Yuning You, Yang Shen

  • Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models

    Matthew Ragoza, Tomohide Masuda, David Koes

  • Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models

    Tomohide Masuda, Matthew Ragoza, David Koes

  • GEFA: Early Fusion Approach in Drug-Target Affinity Prediction

    Tri Minh Nguyen, Thin Nguyen, Thao Minh Le, Truyen Tran

  • Sequence and stucture based deep learning models for the identification of peptide binding sites

    Osama Abdin, Philip Kim, Han Wen

  • Combining variational autoencoder representations with structural descriptors improves prediction of docking scores

    Miguel Garcia-Ortegon, Carl Edward Rasmussen, Andreas Bender, Hiroshi Kajino, Sergio Bacallado

  • SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning

    Jonathan King, David Koes

  • Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization

    Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine

  • Conservative Objective Models: A Simple Approach to Effective Model-Based Optimization

    Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine

Organizers

Raphael
Townshend

Photo of Raphael Townshend
Stanford University

Stephan
Eismann

Photo of Stephan Eismann
Stanford University

Ron
Dror

Photo of Ron Dror
Stanford University

Ellen
Zhong

Photo of Ellen Zhong
MIT

Namrata
Anand

Photo of Namrata Anand
Stanford University

John
Ingraham

Photo of John Ingraham
Generate Biomedicines

Wouter
Boomsma

Photo of Wouter Boomsma
University of Copenhagen

Sergey
Ovchinnikov

Photo of Sergey Ovchinnikov
Harvard University

Roshan
Rao

Photo of Roshan Rao
UC Berkeley

Per
Greisen

Photo of Per Greisen
Novo Nordisk

Rachel
Kolodny

Photo of Rachel Kolodny
University of Haifa

Bonnie
Berger

Photo of Bonnie Berger
MIT

Sponsors

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