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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.
Sat 8:00 a.m. - 8:10 a.m.
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Opening Remarks
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Talk
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Raphael Townshend 🔗 |
Sat 8:12 a.m. - 8:50 a.m.
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Keynote -- Michael Levitt
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Talk
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Michael Levitt 🔗 |
Sat 8:51 a.m. - 9:10 a.m.
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Invited Talk - Charlotte Deane: Predicting the conformational ensembles of proteins
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Talk
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SlidesLive Video » |
Charlotte Deane 🔗 |
Sat 9:11 a.m. - 9:30 a.m.
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Invited Talk - Frank Noe: Deep Markov State Models versus Covid-19
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Talk
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SlidesLive Video » |
Frank Noe 🔗 |
Sat 9:31 a.m. - 9:50 a.m.
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Invited Talk - Andrea Thorn: Finding Secondary Structure in Cryo-EM maps: HARUSPEX
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Talk
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SlidesLive Video » |
Andrea Thorn 🔗 |
Sat 9:50 a.m. - 10:20 a.m.
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Break
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🔗 |
Sat 10:22 a.m. - 11:00 a.m.
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Keynote - David Baker: Rosetta design of COVID antivirals and diagnostics
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Talk
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SlidesLive Video » |
David Baker 🔗 |
Sat 11:00 a.m. - 12:00 p.m.
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Morning Poster Session ( Poster Session ) link » | Ellen Zhong 🔗 |
Sat 12:01 p.m. - 12:11 p.m.
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Contributed Talk - Predicting Chemical Shifts with Graph Neural Networks
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Talk
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SlidesLive Video » |
Ziyue Yang 🔗 |
Sat 12:11 p.m. - 12:21 p.m.
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Contributed Talk - Cryo-ZSSR: multiple-image super-resolution based on deep internal learning
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Talk
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SlidesLive Video » |
Qinwen Huang · Reed Chen · Cynthia Rudin 🔗 |
Sat 12:21 p.m. - 12:31 p.m.
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Contributed Talk - Wasserstein K-Means for Clustering Tomographic Projections
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Talk
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SlidesLive Video » |
Rohan Rao · Amit Moscovich 🔗 |
Sat 12:30 p.m. - 2:00 p.m.
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Lunch + Panel Discussion on Future of ML for Structural Biology (Starts at 1pm)
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Lunch
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Raphael Townshend 🔗 |
Sat 2:01 p.m. - 2:20 p.m.
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Invited Talk - Possu Huang
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Talk
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Possu Huang 🔗 |
Sat 2:20 p.m. - 2:21 p.m.
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Contributed talks intro
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Intro
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Roshan Rao 🔗 |
Sat 2:21 p.m. - 2:31 p.m.
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Contributed Talk - ProGen: Language Modeling for Protein Generation
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Talk
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SlidesLive Video » |
Ali Madani · Bryan McCann · Nikhil Naik · · Possu Huang · Richard Socher 🔗 |
Sat 2:31 p.m. - 2:41 p.m.
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Contributed Talk - Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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Talk
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SlidesLive Video » |
Alexander Rives · Siddharth Goyal · Joshua Meier · Zeming Lin · Demi Guo · Myle Ott · Larry Zitnick · Rob Fergus 🔗 |
Sat 2:41 p.m. - 2:51 p.m.
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Contributed Talk - SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning
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Talk
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SlidesLive Video » |
Jonathan King · David Koes 🔗 |
Sat 2:51 p.m. - 3:01 p.m.
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Contributed Talk - Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models
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Talk
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SlidesLive Video » |
Tomohide Masuda · Matthew Ragoza · David Koes 🔗 |
Sat 3:01 p.m. - 3:11 p.m.
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Contributed Talk - Learning from Protein Structure with Geometric Vector Perceptrons
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Talk
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SlidesLive Video » |
Bowen Jing · Stephan Eismann · Patricia Suriana · Raphael Townshend · Ron Dror 🔗 |
Sat 3:11 p.m. - 4:10 p.m.
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Afternoon Poster Session ( Poster Session ) link » | Roshan Rao 🔗 |
Sat 4:11 p.m. - 4:30 p.m.
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Invited Talk - Mohammed AlQuraishi: (Nearly) end-to-end differentiable learning of protein structure
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Talk
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SlidesLive Video » |
Mohammed AlQuraishi 🔗 |
Sat 4:31 p.m. - 4:50 p.m.
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Invited Talk - Chaok Seok: Ab initio protein structure prediction by global optimization of neural network energy: Can AI learn physics?
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Talk
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SlidesLive Video » |
Chaok Seok 🔗 |
Sat 4:50 p.m. - 5:00 p.m.
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Concluding Remarks
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Talk
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Raphael Townshend 🔗 |
Sat 5:00 p.m. - 6:00 p.m.
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Happy Hour
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Raphael Townshend 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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GEFA: Early Fusion Approach in Drug-Target Affinity Prediction
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Poster Session
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SlidesLive Video » |
Tri Nguyen Minh · Thin Nguyen · Thao M Le · Truyen Tran 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning
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Poster Session
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Nicolas Lopez Carranza · Thomas PIERROT · Joe Phillips · Alexandre Laterre · Amine Kerkeni · Karim Beguir 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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MXMNet: A Molecular Mechanics-Driven Neural Network Based on Multiplex Graph for Molecules
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Poster Session
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Shuo Zhang · Yang Liu 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Protein model quality assessment using rotation-equivariant, hierarchical neural networks
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Poster Session
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SlidesLive Video » |
Stephan Eismann · Patricia Suriana · Bowen Jing · Raphael Townshend · Ron Dror 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Is Transfer Learning Necessary for Protein Landscape Prediction?
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Poster Session
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David Belanger · David Dohan 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Fast and adaptive protein structure representations for machine learning
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Poster Session
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SlidesLive Video » |
Janani Durairaj · Aalt van Dijk 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization
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Poster Session
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Azadeh Alavi 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction
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Poster Session
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Yuning You · Yang Shen 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models
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Poster Session
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SlidesLive Video » |
Matthew Ragoza · Tomohide Masuda · David Koes 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
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Poster Session
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Jesse Vig · Ali Madani 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Exploring generative atomic models in cryo-EM reconstruction
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Poster Session
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Ellen Zhong · Adam Lerer · · Bonnie Berger 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
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Poster Session
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Modestas Filipavicius 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Sequence and stucture based deep learning models for the identification of peptide binding sites
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Poster Session
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SlidesLive Video » |
Osama Abdin · Han Wen 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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ESM-1b: Optimizing Evolutionary Scale Modeling
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Poster Session
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Joshua Meier · Jason Liu · Zeming Lin · Naman Goyal · Myle Ott · Alexander Rives 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
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Poster Session
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SlidesLive Video » |
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Conservative Objective Models: A Simple Approach to Effective Model-Based Optimization
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Poster Session
)
SlidesLive Video » |
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Learning Super-Resolution Electron Density Map of Proteins using 3D U-Net
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Poster Session
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BAISHALI MULLICK · Yuyang Wang · Amir Barati Farimani 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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The structure-fitness landscape of pairwise relations in generative sequence models
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Poster Session
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dylan marshall · Peter Koo · Sergey Ovchinnikov 🔗 |
Sat 6:00 p.m. - 6:00 p.m.
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Combining variational autoencoder representations with structural descriptors improves prediction of docking scores
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Poster Session
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Miguel Garcia-Ortegon · Carl Edward Rasmussen · Hiroshi Kajino 🔗 |
Author Information
Raphael Townshend (Stanford University)
Stephan Eismann (Stanford University)
Ron Dror (Stanford)
Ellen Zhong (Massachusetts Institute of Technology)
Namrata Anand (Stanford University)
John Ingraham (Generate Biomedicines)
Wouter Boomsma (University of Copenhagen)
Sergey Ovchinnikov (Harvard)
Roshan Rao (UC Berkeley)
Per Greisen (Novo Nordisk)
Rachel Kolodny (Haifa University)
Bonnie Berger (MIT)
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