no code implementations • bioRxiv 2022 • Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives
We find that as models are scaled they learn information enabling the prediction of the three-dimensional structure of a protein at the resolution of individual atoms.
1 code implementation • 13 Feb 2021 • Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John F. Canny, Pieter Abbeel, Tom Sercu, Alexander Rives
Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins.
no code implementations • 1 Jan 2021 • Tom Sercu, Robert Verkuil, Joshua Meier, Brandon Amos, Zeming Lin, Caroline Chen, Jason Liu, Yann Lecun, Alexander Rives
We propose the Neural Potts Model objective as an amortized optimization problem.
no code implementations • ICLR 2021 • Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, Alexander Rives
Unsupervised contact prediction is central to uncovering physical, structural, and functional constraints for protein structure determination and design.
1 code implementation • Proceedings of the National Academy of Sciences 2020 • Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation.
1 code implementation • ICLR 2020 • Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives
We propose an energy-based model (EBM) of protein conformations that operates at atomic scale.