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Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protein backbone.
Amino acid substitution matrices from protein blocks
Steven Henikoff and Jorja G Henikoff · 1992
Earlier work this paper cites.
CATH – a hierarchic classification of protein domain structures
CA Orengo, AD Michie, S Jones, DT Jones, MB Swindells, and JM Thornton · 1997
Earlier work this paper cites.
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Protein folding and de novo protein design for biotechnological applications
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Structured denoising diffusion models in discrete state-spaces
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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E(n) equivariant graph neural networks
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Protein design and variant prediction using autoregressive generative models
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
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Protein structure generation via folding diffusion
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