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We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences.
A discussion of the solution for the best rotation to relate two sets of vectors
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Analysis of the antigen combining site: correlations between length and sequence composition of the hypervariable loops and the nature of the antigen
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Sabdab: the structural antibody database
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Deep unsupervised learning using nonequilibrium thermodynamics
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The rosetta all-atom energy function for macromolecular modeling and design
R. F. Alford, A. Leaver-Fay, J. R. Jeliazkov, M. J. O’Meara, F. P. DiMaio, H. Park, M. V. Shapovalov, P. D. Renfrew, V. K. Mulligan, K. Kappel, et al · 2017
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De novo design of a hyperstable non-natural protein–ligand complex with sub-å accuracy
N. F. Polizzi, Y. Wu, T. Lemmin, A. M. Maxwell, S.-Q. Zhang, J. Rawson, D. N. Beratan, M. J. Therien, and W. F. DeGrado · 2017
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Universal function approximation by deep neural nets with bounded width and relu activations
B. Hanin · 2019
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Efficient graph generation with graph recurrent attention networks
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Five computational developability guidelines for therapeutic antibody profiling
M. I. Raybould, C. Marks, K. Krawczyk, B. Taddese, J. Nowak, A. P. Lewis, A. Bujotzek, J. Shi, and C. M. Deane · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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A. Elnaggar, M. Heinzinger, C. Dallago, G. Rihawi, Y. Wang, L. Jones, T. Gibbs, T. Feher, C. Angerer, M. Steinegger, et al · 2020
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Restricted epitope specificity determined by variable region germline segment pairing in rodent antibody repertoires
Y.-C. Hsiao, Y.-J. J. Chen, L. D. Goldstein, J. Wu, Z. Lin, K. Schneider, S. Chaudhuri, A. Antony, K. Bajaj Pahuja, Z. Modrusan, et al · 2020
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Macromolecular modeling and design in rosetta: recent methods and frameworks
J. K. Leman, B. D. Weitzner, S. M. Lewis, J. Adolf-Bryfogle, N. Alam, R. F. Alford, M. Aprahamian, D. Baker, K. A. Barlow, P. Barth, et al · 2020
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A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding
R. Akbar, P. A. Robert, M. Pavlović, J. R. Jeliazkov, I. Snapkov, A. Slabodkin, C. R. Weber, L. Scheffer, E. Miho, I. H. Haff, et al · 2021
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De novo protein design by deep network hallucination
I. Anishchenko, S. J. Pellock, T. M. Chidyausiku, T. A. Ramelot, S. Ovchinnikov, J. Hao, K. Bafna, C. Norn, A. Kang, A. K. Bera, et al · 2021
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Structured denoising diffusion models in discrete state-spaces
J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. van den Berg · 2021
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Sensitive protein alignments at tree-of-life scale using diamond
B. Buchfink, K. Reuter, and H.-G. Drost · 2021
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Function-guided protein design by deep manifold sampling
V. Gligorijević, D. Berenberg, S. Ra, A. Watkins, S. Kelow, K. Cho, and R. Bonneau · 2021
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Highly accurate protein structure prediction with alphafold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, et al · 2021
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Variational diffusion models
D. Kingma, T. Salimans, B. Poole, and J. Ho · 2021
Equivariant graph mechanics networks with constraints
W. Huang, J. Han, Y. Rong, T. Xu, F. Sun, and J. Huang · 2022
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Illuminating protein space with a programmable generative model
J. Ingraham, M. Baranov, Z. Costello, V. Frappier, A. Ismail, S. Tie, W. Wang, V. Xue, F. Obermeyer, A. Beam, et al · 2022
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Equifold: Protein structure prediction with a novel coarse-grained structure representation
J. H. Lee, P. Yadollahpour, A. Watkins, N. C. Frey, A. Leaver-Fay, S. Ra, K. Cho, V. Gligorijevic, A. Regev, and R. Bonneau · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
S. Luo, Y. Su, X. Peng, S. Wang, J. Peng, and J. Ma · 2022
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Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators
K. Martinkus, A. Loukas, N. Perraudin, and R. Wattenhofer · 2022
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Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
D. M. Mason, S. Friedensohn, C. R. Weber, C. Jordi, B. Wagner, S. M. Meng, R. A. Ehling, L. Bonati, J. Dahinden, P. Gainza, et al · 2021
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Pfam: The protein families database in 2021
J. Mistry, S. Chuguransky, L. Williams, M. Qureshi, G. A. Salazar, E. L. Sonnhammer, S. C. Tosatto, L. Paladin, S. Raj, L. J. Richardson, et al · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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Protein sequence design by conformational landscape optimization
C. Norn, B. I. Wicky, D. Juergens, S. Liu, D. Kim, D. Tischer, B. Koepnick, I. Anishchenko, F. Players, D. Baker, et al · 2021
Cited alongside, same era.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma, et al · 2021
Cited alongside, same era.
Deciphering antibody affinity maturation with language models and weakly supervised learning
J. A. Ruffolo, J. J. Gray, and J. Sulam · 2021
Cited alongside, same era.
Reprogramming large pretrained language models for antibody sequence infilling
I. Melnyk, V. Chenthamarakshan, P.-Y. Chen, P. Das, A. Dhurandhar, I. Padhi, and D. Das · 2022
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J. W. Park, S. Stanton, S. Saremi, A. Watkins, H. Dwyer, V. Gligorijevic, R. Bonneau, S. Ra, and K. Cho · 2022
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Frame averaging for invariant and equivariant network design
O. Puny, M. Atzmon, E. J. Smith, I. Misra, A. Grover, H. Ben-Hamu, and Y. Lipman · 2022
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Antibody-antigen binding interface analysis in the big data era
P. B. Reis, G. P. Barletta, L. Gagliardi, S. Fortuna, M. A. Soler, and W. Rocchia · 2022
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Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
J. A. Ruffolo and J. J. Gray · 2022
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Protein sequence and structure co-design with equivariant translation
C. Shi, C. Wang, J. Lu, B. Zhong, and J. Tang · 2022
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Large-scale design and refinement of stable proteins using sequence-only models
J. M. Singer, S. Novotney, D. Strickland, H. K. Haddox, N. Leiby, G. J. Rocklin, C. M. Chow, A. Roy, A. K. Bera, F. C. Motta, et al · 2022
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Accelerating bayesian optimization for biological sequence design with denoising autoencoders
S. Stanton, W. Maddox, N. Gruver, P. Maffettone, E. Delaney, P. Greenside, and A. G. Wilson · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
B. L. Trippe, J. Yim, D. Tischer, T. Broderick, D. Baker, R. Barzilay, and T. Jaakkola · 2022
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Digress: Discrete denoising diffusion for graph generation
C. Vignac, I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard · 2022
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Joint protein sequence-structure co-design via equivariant diffusion
R. Vinod, K. K. Yang, and L. Crawford · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
J. L. Watson, D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles, et al · 2022
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Protein structure generation via folding diffusion
K. E. Wu, K. K. Yang, R. v. d. Berg, J. Y. Zou, A. X. Lu, and A. P. Amini · 2022
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Unsupervised protein-ligand binding energy prediction via neural euler’s rotation equation
W. Jin, S. Sarkizova, X. Chen, N. Hacohen, and C. Uhler · 2023
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Eigenfold: Generative protein structure prediction with diffusion models
B. Jing, E. Erives, P. Pao-Huang, G. Corso, B. Berger, and T. Jaakkola · 2023
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Diffdock-pp: Rigid protein-protein docking with diffusion models
M. A. Ketata, C. Laue, R. Mammadov, H. Stärk, M. Wu, G. Corso, C. Marquet, R. Barzilay, and T. S. Jaakkola · 2023
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Score-based generative modeling for de novo protein design
J. S. Lee, J. Kim, and P. M. Kim · 2023
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Y. Lin and M. AlQuraishi · 2023
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Joint generation of protein sequence and structure with rosettafold sequence space diffusion
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2022 fda approvals
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The resp ai model accelerates the identification of tight-binding antibodies
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Unlocking de novo antibody design with generative artificial intelligence
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Germline-encoded amino acid–binding motifs drive immunodominant public antibody responses
E. L. Shrock, R. T. Timms, T. Kula, E. L. Mena, A. P. West Jr, R. Guo, I.-H. Lee, A. A. Cohen, L. G. McKay, C. Bi, et al · 2023
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