Workshop: Machine Learning for Structural Biology

Raphael Townshend, Stephan Eismann, Ron Dror, Ellen Zhong, Namrata Anand, John Ingraham, Wouter Boomsma, Sergey Ovchinnikov, Roshan Rao, Per Greisen, Rachel Kolodny, Bonnie Berger

2020-12-12T08:00:00-08:00 - 2020-12-12T18:00:00-08:00
Abstract: 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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2020-12-12T08:00:00-08:00 - 2020-12-12T08:10:00-08:00
Opening Remarks
Raphael Townshend
2020-12-12T08:10:00-08:00 - 2020-12-12T08:12:00-08:00
Michael Levitt intro
Raphael Townshend
2020-12-12T08:12:00-08:00 - 2020-12-12T08:50:00-08:00
Keynote -- Michael Levitt
Michael Levitt
2020-12-12T08:50:00-08:00 - 2020-12-12T08:51:00-08:00
Charlotte Deane Intro
Stephan Eismann
2020-12-12T08:51:00-08:00 - 2020-12-12T09:10:00-08:00
Invited Talk 1 -- Charlotte Deane
Charlotte Deane
2020-12-12T09:10:00-08:00 - 2020-12-12T09:11:00-08:00
Frank Noe Intro
Wouter Boomsma
2020-12-12T09:11:00-08:00 - 2020-12-12T09:30:00-08:00
Invited Talk 5 -- Frank Noe
Frank Noe
2020-12-12T09:30:00-08:00 - 2020-12-12T09:31:00-08:00
Andrea Thorn Intro
Ellen Zhong
2020-12-12T09:31:00-08:00 - 2020-12-12T09:50:00-08:00
Invited Talk 2 -- Andrea Thorn
Andrea Thorn
2020-12-12T09:50:00-08:00 - 2020-12-12T10:20:00-08:00
2020-12-12T10:20:00-08:00 - 2020-12-12T10:22:00-08:00
David Baker Intro
Namrata Anand
2020-12-12T10:22:00-08:00 - 2020-12-12T11:00:00-08:00
Keynote -- David Baker
David Baker
2020-12-12T11:00:00-08:00 - 2020-12-12T12:00:00-08:00
Morning Poster Session
Ellen Zhong
2020-12-12T12:00:00-08:00 - 2020-12-12T12:01:00-08:00
Contributed Talks Intro
Ellen Zhong
3 min each
2020-12-12T12:01:00-08:00 - 2020-12-12T12:11:00-08:00
Contributed Talk - Predicting Chemical Shifts with Graph Neural Networks
Ziyue Yang
2020-12-12T12:11:00-08:00 - 2020-12-12T12:21:00-08:00
Contributed Talk - Cryo-ZSSR: multiple-image super-resolution based on deep internal learning
Wendy Huang, Reed Chen, Cynthia Rudin
2020-12-12T12:21:00-08:00 - 2020-12-12T12:31:00-08:00
Contributed Talk - Wasserstein K-Means for Clustering Tomographic Projections
Rohan Rao, Amit Moscovich
2020-12-12T12:30:00-08:00 - 2020-12-12T14:00:00-08:00
Raphael Townshend
2020-12-12T14:00:00-08:00 - 2020-12-12T14:01:00-08:00
Possu Huang intro
Namrata Anand
2020-12-12T14:01:00-08:00 - 2020-12-12T14:20:00-08:00
Invited Talk 4 -- Possu Huang
Possu Huang
2020-12-12T14:20:00-08:00 - 2020-12-12T14:21:00-08:00
Contributed talks intro
Roshan Rao
2020-12-12T14:21:00-08:00 - 2020-12-12T14:31:00-08:00
Contributed Talk - ProGen: Language Modeling for Protein Generation
Ali Madani, Bryan McCann, Nikhil Naik, , Possu Huang, Richard Socher
2020-12-12T14:31:00-08:00 - 2020-12-12T14:41:00-08:00
Contributed Talk - Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Siddharth Goyal, Zeming Lin, Demi Guo, Myle Ott, Larry Zitnick, Rob Fergus
2020-12-12T14:41:00-08:00 - 2020-12-12T14:51:00-08:00
Contributed Talk - SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning
Jonathan King, Dave Koes
2020-12-12T14:51:00-08:00 - 2020-12-12T15:01:00-08:00
Contributed Talk - Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models
Tomohide Masuda, Matthew Ragoza, Dave Koes
2020-12-12T15:01:00-08:00 - 2020-12-12T15:11:00-08:00
Contributed Talk - Learning from Protein Structure with Geometric Vector Perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael Townshend, Ron Dror
2020-12-12T15:11:00-08:00 - 2020-12-12T16:10:00-08:00
Afternoon Poster Session
Roshan Rao
2020-12-12T16:10:00-08:00 - 2020-12-12T16:11:00-08:00
Mohammed AlQuraishi intro
Raphael Townshend
2020-12-12T16:11:00-08:00 - 2020-12-12T16:30:00-08:00
Invited Talk 3 -- Mohammed AlQuraishi
Mohammed AlQuraishi
2020-12-12T16:30:00-08:00 - 2020-12-12T16:31:00-08:00
Chaok Seok intro
Sergey Ovchinnikov
2020-12-12T16:31:00-08:00 - 2020-12-12T16:50:00-08:00
Invited Talk 6 -- Chaok Seok
Chaok Seok
2020-12-12T16:50:00-08:00 - 2020-12-12T17:00:00-08:00
Concluding Remarks
Raphael Townshend
2020-12-12T17:00:00-08:00 - 2020-12-12T18:00:00-08:00
Happy Hour
Raphael Townshend
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Fast and adaptive protein structure representations for machine learning
Janani Durairaj, Aalt van Dijk
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
The structure-fitness landscape of pairwise relations in generative sequence models
dylan marshall, Peter Koo, Sergey Ovchinnikov
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Conservative Objective Models: A Simple Approach to Effective Model-Based Optimization
Brandon Trabucco, Aviral Kumar, XINYANG GENG, Sergey Levine
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Exploring generative atomic models in cryo-EM reconstruction
Ellen Zhong, Adam Lerer, , Bonnie Berger
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Combining variational autoencoder representations with structural descriptors improves prediction of docking scores
Miguel Garcia Ortegon, Carl Edward Rasmussen, Hiroshi Kajino
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
Modestas Filipavicius
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
DHS-Crystallize: Deep-Hybrid-Sequence based method for predicting protein Crystallization
Azadeh Alavi
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Learning Super-Resolution Electron Density Map of Proteins using 3D U-Net
BAISHALI MULLICK, Yuyang Wang, Amir Barati Farimani
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
Jesse Vig, Ali Madani
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
Stephan Eismann, Patricia Suriana, Bowen Jing, Raphael Townshend, Ron Dror
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Sequence and stucture based deep learning models for the identification of peptide binding sites
Osama Abdin, Han Wen
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning
Nicolas Lopez Carranza, Thomas PIERROT, Joe Phillips, Alex Laterre, Amine Kerkeni, Karim Beguir
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models
Matthew Ragoza, Tomohide Masuda, Dave Koes
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction
Yuning You, Yang Shen
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
MXMNet: A Molecular Mechanics-Driven Neural Network Based on Multiplex Graph for Molecules
Shuo Zhang, Yang Liu
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Brandon Trabucco, Aviral Kumar, XINYANG GENG, Sergey Levine
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
Is Transfer Learning Necessary for Protein Landscape Prediction?
David Belanger, David Dohan
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
ESM-1b: Optimizing Evolutionary Scale Modeling
Jason Liu, Zeming Lin, Naman Goyal, Myle Ott, Alexander Rives
2020-12-12T18:00:00-08:00 - 2020-12-12T18:00:00-08:00
GEFA: Early Fusion Approach in Drug-Target Affinity Prediction
Tri Nguyen Minh, Thin Nguyen, Thao M Le, Truyen Tran