Search Results for author:
Found 6 papers, 3 papers with code
Language models of protein sequences at the scale of evolution enable accurate structure prediction
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.
MSA Transformer
Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins.
Neural Potts Model
We propose the Neural Potts Model objective as an amortized optimization problem.
Transformer protein language models are unsupervised structure learners
Unsupervised contact prediction is central to uncovering physical, structural, and functional constraints for protein structure determination and design.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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.
Energy-based models for atomic-resolution protein conformations
We propose an energy-based model (EBM) of protein conformations that operates at atomic scale.
