We welcome submissions of short papers leveraging machine learning to address problems in structural biology, including but not limited to:
We request anonymized PDF submissions by Thursday, October 15th, 2020, 11:59PM in the timezone of your choice through Microsoft CMT.
Submissions should be 4-9 pages in PDF format and fully anonymized as per the requirements of NeurIPS. We request use of the NeurIPS style files. A maximum of 9 pages excluding references and appendices will be considered. The review process will be double-blind.
Accepted papers will be invited to present a poster at the virtual 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.
Submission Deadline: October 15, 2020, 11:59 PM, Anywhere on Earth.
Notification of Acceptance: Oct 30, 2020.
Workshop Date: December 12th, 2020
|08:10||Keynote - Michael Levitt:
Is Basic Science Needed for Significant and Fundamental Discoveries
|08:50||Invited Talk - Charlotte Deane:
Predicting the conformational ensembles of proteins
|9:10||Invited Talk - Frank Noe:
Deep Markov State Models versus Covid-19
|9:30||Invited Talk - Andrea Thorn:
Finding Secondary Structure in Cryo-EM maps: HARUSPEX
|10:20||Keynote - David Baker:
Rosetta design of COVID antivirals and diagnostics
|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 SessionHeld on gather.town|
|01:00||Panel Discussion: Future of ML for Structural Biology|
|2:00||Invited Talk - Possu Huang:Jump starting an evolution by protein design through deep learning of protein structures|
|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 Held on gather.town|
|4:10||Invited Talk - Mohammed AlQuraishi:
(Nearly) end-to-end differentiable learning of protein structure
|4:30||Invited Talk - Chaok Seok: Ab initio protein structure prediction by global optimization of neural network energy: Can AI learn physics?|
|5:00||Happy HourHeld on gather.town|