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Representation learning and \emph{de novo} generation of proteins are pivotal computational biology tasks.
CATH – a hierarchic classification of protein domain structures
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"The Protein Data Bank"
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Tm-align: a protein structure alignment algorithm based on the tm-score
Zhang Y. and Skolnick J · 2005
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"the gene ontology (GO) project in 2006"
Gene Ontology Consortium · 2006
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MaxCluster: a tool for protein structure comparison and clustering
A. Herbert and M. Sternberg · 2008
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How significant is a protein structure similarity with TM-score = 0.5?
J. Xu and Zhang Y · 2010
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Improved variational inference with inverse autoregressive flow
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling · 2016
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Small molecules, big targets: drug discovery faces the protein–protein interaction challenge
D. E. Scott, A. R. Bayly, C. Abell, and J. Skidmore · 2016
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Neural discrete representation learning
A. van den Oord, O. Vinyals, and K. Kavukcuoglu · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Improving language understanding by generative pre-training
A. Radford and K. Narasimhan · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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Generative models for graph-based protein design
J. Ingraham, V. Garg, R. Barzilay, and T. Jaakkola · 2019
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Better language models and their implications
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
A. Razavi, A. van den Oord, and O. Vinyals · 2019
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Self-supervised multimodal versatile networks
J.-B. Alayrac, A. Recasens, R. Schneider, R. Arandjelović, J. Ramapuram, J. De Fauw, L. Smaira, S. Dieleman, and A. Zisserman · 2020
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Generative pretraining from pixels
M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever · 2020
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The curious case of neural text degeneration
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi · 2020
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Accurate prediction of protein structures and interactions using a three-track neural network
M. Baek, F. DiMaio, I. Anishchenko, J. Dauparas, S. Ovchinnikov, G. R. Lee, J. Wang, Q. Cong, L. N. Kinch, R. D. Schaeffer, C. Millán, H. Park, C. Adams, C. R. Glassman, A. DeGiovanni, J. H. Pereira, A. V. Rodrigues, A. A. van Dijk, A. C. Ebrecht, D. J. Opperman, T. Sagmeister, C. Buhlheller, T. Pavkov-Keller, M. K. Rathinaswamy, U. Dalwadi, C. K. Yip, J. E. Burke, K. C. Garcia, N. V. Grishin, P. D. Adams, R. J. Read, and D. Baker · 2021
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Decision transformer: Reinforcement learning via sequence modeling
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch · 2021
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Structure-based protein function prediction using graph convolutional networks
V. Gligorijević, P. D. Renfrew, T. Kosciolek, J. K. Leman, D. Berenberg, T. Vatanen, C. Chandler, B. C. Taylor, I. M. Fisk, H. Vlamakis, R. J. Xavier, R. Knight, K. Cho, and R. Bonneau · 2021
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Learning from protein structure with geometric vector perceptrons
B. Jing, S. Eismann, P. Suriana, R. J. L. Townshend, and R. Dror · 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, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli, and D. Hassabis · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al · 2021
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Flamingo: a visual language model for few-shot learning
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds, et al · 2022
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MaskGIT: Masked generative image transformer
H. Chang, H. Zhang, L. Jiang, C. Liu, and W. T. Freeman · 2022
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Robust deep learning–based protein sequence design using proteinmpnn
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. Ragotte, L. Milles, B. Wicky, A. Courbet, R. Haas, N. Bethel, P. Leung, T. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. Bera, N.P. King, and D. Baker · 2022
Cited alongside, same era.
"Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation"
R. R. "Eguchi, C. Choe, and P.-S. Huang · 2022
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Pesto: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
L. F. Krapp, L. A. Abriata, F. Cortés Rodriguez, and M. Dal Peraro · 2023
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Proteinshake: Building datasets and benchmarks for deep learning on protein structures
T. Kucera, C. Oliver, D. Chen, and K. Borgwardt · 2023
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Diffusion in a quantized vector space generates non-idealized protein structures and predicts conformational distributions
Y. Liu, L. Chen, and H. Liu · 2023
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Contrasting sequence with structure: Pre-training graph representations with PLMs
L. Robinson, T. Atkinson, L. Copoiu, P. Bordes, T. Pierrot, and T. Barrett · 2023
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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
B. Trippe, J. Yim, D. Tischer, D. Baker, T. Broderick, R. Barzilay, and T. S. Jaakkola · 2023
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Protgpt2 is a deep unsupervised language model for protein design
N. Ferruz, S. Schmidt, and B. Höcker · 2022
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Independent SE(3)-equivariant models for end-to-end rigid protein docking
O.-E. Ganea, X. Huang, C. Bunne, Y. Bian, R. Barzilay, T. S. Jaakkola, and A. Krause · 2022
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An empirical analysis of compute-optimal large language model training
J. Hoffmann, S. Borgeaud, A. Mensch, E. Buchatskaya, T. Cai, E. Rutherford, D. de Las Casas, L. A. Hendricks, J. Welbl, A. Clark, T. Hennigan, E. Noland, K. Millican, G. van den Driessche, B. Damoc, A. Guy, S. Osindero, K. Simonyan, E. Elsen, O. Vinyals, J. Rae, and L. Sifre · 2022
Cited alongside, same era.
Learning inverse folding from millions of predicted structures
C. Hsu, R. Verkuil, J. Liu, Z. Lin, B. Hie, T. Sercu, A. Lerer, and A. Rives · 2022
Cited alongside, same era.
Language models of protein sequences at the scale of evolution enable accurate structure prediction
Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, N. Smetanin, A. dos Santos Costa, M. Fazel-Zarandi, T. Sercu, S. Candido, et al · 2022
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A generalist agent
S. Reed, K. Zolna, E. Parisotto, S. Gomez Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar, and N. de Freitas · 2022
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Current progress and open challenges for applying deep learning across the biosciences
N. Sapoval, A. Aghazadeh, M. G. Nute, D. A. Antunes, A. Balaji, R. Baraniuk, C. J. Barberan, R. Dannenfelser, C. Dun, M. Edrisi, et al · 2022
Cited alongside, same era.
De novo design of protein structure and function with RFdiffusion
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, B. I. M. Wicky, N. Hanikel, S. J. Pellock, A. Courbet, W. Sheffler, J. Wang, P. Venkatesh, I. Sappington, S. Vázquez Torres, A Lauko, V. De Bortoli, E. Mathieu, S. Ovchinnikov, R. Barzilay, T. S. Jaakkola, F. DiMaio, M. Baek, and D. Baker · 2023
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SE(3) diffusion model with application to protein backbone generation
J. Yim, B. Trippe, V. De Bortoli, E. Mathieu, A. Doucet, R. Barzilay, and T. Jaakkola · 2023
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Discrete graph auto-encoder
Y. Boget, M. Gregorova, and A. Kalousis · 2024
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SE(3)-stochastic flow matching for protein backbone generation
J. Bose, T. Akhound-Sadegh, K. Fatras, G. Huguet, J. Rector-Brooks, C.-H. Liu, A. C. Nica, M. Korablyov, M. M. Bronstein, and A. Tong · 2024
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Simulating 500 million years of evolution with a language model
T. Hayes, R. Rao, H. Akin, N. J. Sofroniew, D. Oktay, Z. Lin, R. Verkuil, V. Q. Tran, J. Deaton, M. Wiggert, R. Badkundri, I. Shafkat, J. Gong, A. Derry, R. S. Molina, N. Thomas, Y. Khan, C. Mishra, C. Kim, L. J. Bartie, M. Nemeth, P. D. Hsu, T. Sercu, S. Candido, and A. Rives · 2024
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Deep learning for protein structure prediction and design—progress and applications
J. Jänes and P. Beltrao · 2024
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Prosst: Protein language modeling with quantized structure and disentangled attention
M. Li, Y. Tan, X. Ma, B. Zhong, Z. Zhou, H. Yu, W. Ouyang, L Hong, B Zhou, and P Tan · 2024
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Structure language models for protein conformation generation, 2024
J. Lu, X. Chen, S. Z. Lu, C. Shi, H. Guo, Y. Bengio, and J. Tang · 2024
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Finite scalar quantization: VQ-VAE made simple
F. Mentzer, D. Minnen, E. Agustsson, and M. Tschannen · 2024
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Chatnt: A multimodal conversational agent for dna, rna and protein tasks
G. Richard, B. P. de Almeida, H. Dalla-Torre, C. Blum, L. Hexemer, P. Pandey, S. Laurent, M. Lopez, A. Laterre, M. Lang, et al · 2024
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Saprot: Protein language modeling with structure-aware vocabulary
J. Su, C. Han, Y. Zhou, J. Shan, X. Zhou, and F. Yuan · 2024
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HQ-VAE: Hierarchical discrete representation learning with variational bayes
Y. Takida, Y. Ikemiya, T. Shibuya, K. Shimada, W. Choi, C. H. Lai, N. Murata, T. Uesaka, K. Uchida, W.-H. Liao, and Y. Mitsufuji · 2024
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Towards generalist biomedical AI
T. Tu, S. Azizi, D. Driess, M. Schaekermann, M. Amin, P. Chang, A. Carroll, C. Lau, R. Tanno, I. Ktena, and o. others · 2024
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Fast and accurate protein structure search with foldseek
M. van Kempen, S. S. Kim, C. Tumescheit, M. Mirdita, J. Lee, C. L. M. Gilchrist, and J. Söding · 2024
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Protein structure generation via folding diffusion
K. E. Wu, K/ K. Yang, R. van den Berg, S. Alamdari, J. Y. Zou, A. X. Lu, and A. P. Amini · 2024
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Me llama: Foundation large language models for medical applications
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Language model beats diffusion - tokenizer is key to visual generation
L. Yu, J. Lezama, N. B. Gundavarapu, L. Versari, K. Sohn, D. Minnen, Y. Cheng, A. Gupta, X. Gu, A. G. Hauptmann, B. Gong, M.-H. Yang, I. Essa, D. A. Ross, and L. Jiang · 2024
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Masked audio generation using a single non-autoregressive transformer
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