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Found 8 papers, 5 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.
Language models enable zero-shot prediction of the effects of mutations on protein function
Modeling the effect of sequence variation on function is a fundamental problem for understanding and designing proteins.
MSA Transformer
Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins.
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.
Single Layers of Attention Suffice to Predict Protein Contacts
On the one hand, factored attention is a direct simplification of multihead scaled dot-product attention in the Transformer.
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning.
Evaluating Protein Transfer Learning with TAPE
Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques.
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples.
