Workshop
Machine Learning in Structural Biology
Ellen Zhong 路 Raphael Townshend 路 Stephan Eismann 路 Namrata Anand 路 Roshan Rao 路 John Ingraham 路 Wouter Boomsma 路 Sergey Ovchinnikov 路 Bonnie Berger
Mon 13 Dec, 6 a.m. PST
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, modeling 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.
Schedule
Mon 6:00 a.m. - 6:10 a.m.
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Opening remarks
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Opening remarks
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SlidesLive Video |
馃敆 |
Mon 6:10 a.m. - 6:30 a.m.
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Invited Talk 1: Michael Bronstein: Geometric deep learning for functional protein design
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Invited talk
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SlidesLive Video |
Michael Bronstein 馃敆 |
Mon 6:30 a.m. - 6:50 a.m.
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Invited Talk 2: Cecilia Clementi: Designing molecular models by machine learning and experimental data
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Invited talk
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SlidesLive Video |
Cecilia Clementi 馃敆 |
Mon 6:50 a.m. - 7:10 a.m.
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Invited Talk 3: Lucy Colwell: Using deep learning to annotate the protein universe
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Invited talk
)
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SlidesLive Video |
Lucy Colwell 馃敆 |
Mon 7:10 a.m. - 7:20 a.m.
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Structure-aware generation of drug-like molecules
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Oral
)
>
SlidesLive Video |
Pavol Drotar 路 Arian Jamasb 路 Ben Day 路 Catalina Cangea 路 Pietro Li贸 馃敆 |
Mon 7:20 a.m. - 7:30 a.m.
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Learning physics confers pose-sensitivity in structure-based virtual screening
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Oral
)
>
SlidesLive Video |
Pawel Gniewek 路 Bradley Worley 路 Kate Stafford 路 Brandon Anderson 路 Henry van den Bedem 馃敆 |
Mon 7:30 a.m. - 7:40 a.m.
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Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs
(
Oral
)
>
SlidesLive Video |
13 presentersDan Rosenbaum 路 Marta Garnelo 路 Michal Zielinski 路 Charles Beattie 路 Ellen Clancy 路 Andrea Huber 路 Pushmeet Kohli 路 Andrew Senior 路 John Jumper 路 Carl Doersch 路 S. M. Ali Eslami 路 Olaf Ronneberger 路 Jonas Adler |
Mon 7:40 a.m. - 7:50 a.m.
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Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-EM
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Oral
)
>
SlidesLive Video |
Qinwen Huang 路 Alberto Bartesaghi 路 Ye Zhou 路 Hsuan-fu Liu 馃敆 |
Mon 7:50 a.m. - 8:30 a.m.
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Keynote 1: John Jumper: Highly accurate protein structure prediction with AlphaFold
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Keynote speaker
)
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SlidesLive Video |
John Jumper 馃敆 |
Mon 8:30 a.m. - 9:30 a.m.
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Poster Session 1
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Poster Session
)
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馃敆 |
Mon 9:30 a.m. - 10:30 a.m.
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Panel Discussion
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Panel Discussion
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SlidesLive Video |
馃敆 |
Mon 10:30 a.m. - 11:10 a.m.
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Keynote 2: Jane Richardson: The Very Early Days of Structural Biology before ML
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Keynote speaker
)
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SlidesLive Video |
Jane S Richardson 馃敆 |
Mon 11:10 a.m. - 11:20 a.m.
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Break
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馃敆 |
Mon 11:20 a.m. - 11:30 a.m.
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Predicting cryptic pocket opening from protein structures using graph neural networks
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Oral
)
>
SlidesLive Video |
Artur Meller 路 Michael Ward 路 Meghana Kshirsagar 路 Felipe Oviedo 路 Jonathan Borowsky 路 Juan Lavista Ferres 路 Greg Bowman 馃敆 |
Mon 11:30 a.m. - 11:40 a.m.
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End-to-end learning of multiple sequence alignmentswith differentiable Smith-Waterman
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Oral
)
>
SlidesLive Video |
Samantha Petti 路 Nicholas Bhattacharya 路 Roshan Rao 路 Justas Dauparas 路 Neil Thomas 路 Juannan Zhou 路 Alexander Rush 路 Peter Koo 路 Sergey Ovchinnikov 馃敆 |
Mon 11:40 a.m. - 11:50 a.m.
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Function-guided protein design by deep manifold sampling
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Oral
)
>
SlidesLive Video |
Vladimir Gligorijevic 路 Stephen Ra 路 Dan Berenberg 路 Richard Bonneau 路 Kyunghyun Cho 馃敆 |
Mon 11:50 a.m. - 12:00 p.m.
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Deciphering antibody affinity maturation with language models and weakly supervised learning
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Oral
)
>
SlidesLive Video |
Jeffrey Ruffolo 路 Jeffrey Gray 路 Jeremias Sulam 馃敆 |
Mon 12:00 p.m. - 12:10 p.m.
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Deep generative models create new and diverse protein structures
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Oral
)
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SlidesLive Video |
Zeming Lin 路 Tom Sercu 路 yann lecun 路 Alex Rives 馃敆 |
Mon 12:10 p.m. - 1:10 p.m.
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Poster Session 2
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Poster Session
)
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馃敆 |
Mon 1:10 p.m. - 1:30 p.m.
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Invited Talk 4: Derek Lowe: AI and ML in Drug Discovery, a Chemist鈥檚 perspective.
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Invited talk
)
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SlidesLive Video |
Derek Lowe 馃敆 |
Mon 1:30 p.m. - 1:50 p.m.
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Invited Talk 5: Regina Barzilay: Infusing biology into molecular models for property prediction
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Invited talk
)
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SlidesLive Video |
Regina Barzilay 馃敆 |
Mon 1:50 p.m. - 2:10 p.m.
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Invited Talk 6: Amy Keating: Navigating landscapes of protein interaction specificity using data-driven models
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Invited talk
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SlidesLive Video |
Amy Keating 馃敆 |
Mon 2:10 p.m. - 2:20 p.m.
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Closing remarks
(
Closing remarks
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SlidesLive Video |
馃敆 |
Mon 2:20 p.m. - 4:00 p.m.
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Social hour
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Social hour
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馃敆 |
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Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction
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Poster
)
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SlidesLive Video |
Natalia Zenkova 路 Ekaterina Sedykh 路 Timofei Ermak 路 Tatiana Shugaeva 路 Vladislav Strashko 路 Aleksei Shpilman 馃敆 |
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MOLUCINATE: A Generative Model for Molecules in 3D Space
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Poster
)
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SlidesLive Video |
Michael Brocidiacono 路 David Koes 馃敆 |
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Active site sequence representation of human kinases outperforms full sequence for affinity prediction
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Poster
)
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SlidesLive Video |
Jannis Born 路 Tien Huynh 路 Astrid Stroobants 路 Wendy Cornell 路 Matteo Manica 馃敆 |
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Interpretable Pairwise Distillations for Generative Protein Sequence Models
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Poster
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Christoph Feinauer 路 Barth茅l茅my Meynard 路 Carlo Lucibello 馃敆 |
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Dock2D: Toy datasets for the molecular recognition problem
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Poster
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SlidesLive Video |
Georgy Derevyanko 路 Sid Bhadra-Lobo 路 Guillaume Lamoureux 馃敆 |
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Studying signal peptides with attention neural networks informs cleavage site predictions
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Poster
)
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SlidesLive Video |
Patrick Bryant 路 Arne Elofsson 馃敆 |
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Protein sequence sampling and prediction from structural data
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Poster
)
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SlidesLive Video |
Gabriel Orellana 路 Javier Caceres-Delpiano 路 Roberto Ibanez 路 Michael P Dunne 路 Leonardo Alvarez 馃敆 |
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Turning high-throughput structural biology into predictive drug design
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Poster
)
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SlidesLive Video |
Kadi Saar 路 Daren Fearon 路 John Chodera 路 Frank von Delft 路 Alpha Lee 馃敆 |
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DLA-Ranker: Evaluating protein docking conformations with many locally oriented cubes
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Poster
)
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SlidesLive Video |
Yasser Mohseni Behbahani 路 Elodie Laine 路 Alessandra Carbone 馃敆 |
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Predicting single-point mutational effect on protein stability
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Poster
)
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SlidesLive Video |
Simon Chu 路 Justin Siegel 馃敆 |
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Exploring 鈭嗏垎G prediction with Siamese Networks
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Poster
)
>
SlidesLive Video |
Andrew McNutt 路 David Koes 馃敆 |
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Residue characterization on AlphaFold2 protein structures using graph neural networks
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Poster
)
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SlidesLive Video |
Nasim Abdollahi 馃敆 |
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A kernel for continuously relaxed, discrete Bayesian optimization of protein sequences
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Poster
)
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Yevgen Zainchkovskyy 路 Simon Bartels 路 S酶ren Hauberg 路 Jes Frellsen 路 Wouter Boomsma 馃敆 |
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HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints
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Poster
)
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SlidesLive Video |
Xuezhi Xie 路 Philip Kim 馃敆 |
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Adapting protein language models for rapid DTI prediction
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Poster
)
>
SlidesLive Video |
Samuel Sledzieski 路 Rohit Singh 路 Lenore J Cowen 路 Bonnie Berger 馃敆 |
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Generative Language Modeling for Antibody Design
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Poster
)
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Richard Shuai 路 Jeffrey Ruffolo 路 Jeffrey Gray 馃敆 |
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TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs
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Poster
)
>
SlidesLive Video |
Alex Li 路 Vikram Sundar 路 Gevorg Grigoryan 路 Amy Keating 馃敆 |
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MSA-Conditioned Generative Protein Language Models for Fitness Landscape Modelling and Design
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Poster
)
>
SlidesLive Video |
Alex Hawkins-Hooker 路 David Jones 路 Brooks Paige 馃敆 |
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Real-valued Sidechain Dihedrals Prediction Using Relation-Shape Convolution
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Poster
)
>
SlidesLive Video |
Xiyao Long 路 Roland Dunbrack 路 Maxim Shapovalov 馃敆 |
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AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design
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Poster
)
>
SlidesLive Video |
Shuhao Zhang 路 Youjun Xu 路 Jianfeng Pei 路 Luhua Lai 馃敆 |
-
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Predicting cryptic pocket opening from protein structures using graph neural networks
(
Poster
)
>
|
Artur Meller 路 Michael Ward 路 Meghana Kshirsagar 路 Felipe Oviedo 路 Jonathan Borowsky 路 Juan Lavista Ferres 路 Greg Bowman 馃敆 |
-
|
Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs
(
Poster
)
>
|
13 presentersDan Rosenbaum 路 Marta Garnelo 路 Michal Zielinski 路 Charles Beattie 路 Ellen Clancy 路 Andrea Huber 路 Pushmeet Kohli 路 Andrew Senior 路 John Jumper 路 Carl Doersch 路 S. M. Ali Eslami 路 Olaf Ronneberger 路 Jonas Adler |
-
|
Structure-aware generation of drug-like molecules
(
Poster
)
>
|
Pavol Drotar 路 Arian Jamasb 路 Ben Day 路 Catalina Cangea 路 Pietro Li贸 馃敆 |
-
|
Deciphering antibody affinity maturation with language models and weakly supervised learning
(
Poster
)
>
|
Jeffrey Ruffolo 路 Jeffrey Gray 路 Jeremias Sulam 馃敆 |
-
|
Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-EM
(
Poster
)
>
|
Qinwen Huang 路 Alberto Bartesaghi 路 Ye Zhou 路 Hsuan-fu Liu 馃敆 |
-
|
Deep generative models create new and diverse protein structures
(
Poster
)
>
|
Zeming Lin 路 Tom Sercu 路 yann lecun 路 Alex Rives 馃敆 |
-
|
End-to-end learning of multiple sequence alignmentswith differentiable Smith-Waterman
(
Poster
)
>
|
Samantha Petti 路 Nicholas Bhattacharya 路 Roshan Rao 路 Justas Dauparas 路 Neil Thomas 路 Juannan Zhou 路 Alexander Rush 路 Peter Koo 路 Sergey Ovchinnikov 馃敆 |
-
|
Learning physics confers pose-sensitivity in structure-based virtual screening
(
Poster
)
>
|
Pawel Gniewek 路 Bradley Worley 路 Kate Stafford 路 Brandon Anderson 路 Henry van den Bedem 馃敆 |
-
|
Function-guided protein design by deep manifold sampling
(
Poster
)
>
|
Vladimir Gligorijevic 路 Stephen Ra 路 Dan Berenberg 路 Richard Bonneau 路 Kyunghyun Cho 馃敆 |