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Proteins are essential macromolecules defined by their amino acid sequences, which determine their three-dimensional structures and, consequently, their functions in all living organisms.
The protein data bank
Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne · 2000
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2020
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Self-supervised contrastive learning of protein representations by mutual information maximization
Amy X Lu, Haoran Zhang, Marzyeh Ghassemi, and Alan Moses · 2020
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Transforming the language of life: transformer neural networks for protein prediction tasks
Ananthan Nambiar, Maeve Heflin, Simon Liu, Sergei Maslov, Mark Hopkins, and Anna Ritz · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Udsmprot: universal deep sequence models for protein classification
Nils Strodthoff, Patrick Wagner, Markus Wenzel, and Wojciech Samek · 2020
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Profile prediction: An alignment-based pre-training task for protein sequence models
Pascal Sturmfels, Jesse Vig, Ali Madani, and Nazneen Fatema Rajani · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Prottrans: Toward understanding the language of life through self-supervised learning
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rehawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al · 2021
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Pre-training co-evolutionary protein representation via a pairwise masked language model
Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, et al · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Iterative refinement graph neural network for antibody sequence-structure co-design
Wengong Jin, Jeremy Wohlwend, Regina Barzilay, and Tommi S Jaakkola · 2021
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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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Deep neural language modeling enables functional protein generation across families
Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos Jr, Caiming Xiong, Zachary Z Sun, Richard Socher, et al · 2021
Cited alongside, same era.
Adversarial contrastive pre-training for protein sequences
Matthew McDermott, Brendan Yap, Harry Hsu, Di Jin, and Peter Szolovits · 2021
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Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alex Rives · 2021
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Pre-training of deep bidirectional protein sequence representations with structural information
Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee, and Sungroh Yoon · 2021
Cited alongside, same era.
Tripletprot: deep representation learning of proteins based on siamese networks
Esmaeil Nourani, Ehsaneddin Asgari, Alice C McHardy, and Mohammad RK Mofrad · 2021
Masked inverse folding with sequence transfer for protein representation learning
Kevin K Yang, Niccolò Zanichelli, and Hugh Yeh · 2022
Later among the works it cites.
Protein generation with evolutionary diffusion: sequence is all you need
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex Xijie Lu, Nicolo Fusi, Ava Pardis Amini, and Kevin K Yang · 2023
Later among the works it cites.
Illuminating protein space with a programmable generative model
John B Ingraham, Max Baranov, Zak Costello, Karl W Barber, Wujie Wang, Ahmed Ismail, Vincent Frappier, Dana M Lord, Christopher Ng-Thow-Hing, Erik R Van Vlack, et al · 2023
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Yeqing Lin and Mohammed AlQuraishi · 2023
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Diffusion in a quantized vector space generates non-idealized protein structures and predicts conformational distributions
Haiyan Liu, Yufeng Liu, and Linghui Chen · 2023
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Cited alongside, same era.
Modeling protein using large-scale pretrain language model
Yijia Xiao, Jiezhong Qiu, Ziang Li, Chang-Yu Hsieh, and Jie Tang · 2021
Cited alongside, same era.
Proteinbert: a universal deep-learning model of protein sequence and function
Nadav Brandes, Dan Ofer, Yam Peleg, Nadav Rappoport, and Michal Linial · 2022
Cited alongside, same era.
Robust deep learning–based protein sequence design using proteinmpnn
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
Cited alongside, same era.
Protgpt2 is a deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, and Birte Höcker · 2022
Cited alongside, same era.
Learning inverse folding from millions of predicted structures
Chloe Hsu, Robert Verkuil, Jason Liu, Zeming Lin, Brian Hie, Tom Sercu, Adam Lerer, and Alexander Rives · 2022
Cited alongside, same era.
Conditional antibody design as 3d equivariant graph translation
Xiangzhe Kong, Wenbing Huang, and Yang Liu · 2022
Cited alongside, same era.
Proteinsgm: Score-based generative modeling for de novo protein design
Jin Sub Lee, Jisun Kim, and Philip M Kim · 2022
Cited alongside, same era.
Later among the works it cites.
Gpt-4 technical report, 2023
OpenAI · 2023
Later among the works it cites.
Saprot: Protein language modeling with structure-aware vocabulary
Jin Su, Chenchen Han, Yuyang Zhou, Junjie Shan, Xibin Zhou, and Fajie Yuan · 2023
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Fast and accurate protein structure search with foldseek
Michel van Kempen, Stephanie S Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron LM Gilchrist, Johannes Söding, and Martin Steinegger · 2023
Later among the works it cites.
De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
Later among the works it cites.
Se (3) diffusion model with application to protein backbone generation
Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola · 2023
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Language model beats diffusion-tokenizer is key to visual generation
Lijun Yu, Jose Lezama, Nitesh Bharadwaj Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al · 2023
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Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola · 2024
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An all-atom protein generative model
Alexander E Chu, Jinho Kim, Lucy Cheng, Gina El Nesr, Minkai Xu, Richard W Shuai, and Po-Ssu Huang · 2024
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Foldtoken: Learning protein language via vector quantization and beyond
Zhangyang Gao, Cheng Tan, Jue Wang, Yufei Huang, Lirong Wu, and Stan Z Li · 2024
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Simulating 500 million years of evolution with a language model
Tomas Hayes, Roshan Rao, Halil Akin, Nicholas J Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q Tran, Jonathan Deaton, Marius Wiggert, et al · 2024
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Sequence-augmented se (3)-flow matching for conditional protein backbone generation
Guillaume Huguet, James Vuckovic, Kilian Fatras, Eric Thibodeau-Laufer, Pablo Lemos, Riashat Islam, Cheng-Hao Liu, Jarrid Rector-Brooks, Tara Akhound-Sadegh, Michael Bronstein, et al · 2024
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Tokenized and continuous embedding compressions of protein sequence and structure
Amy X Lu, Wilson Yan, Kevin K Yang, Vladimir Gligorijevic, Kyunghyun Cho, Pieter Abbeel, Richard Bonneau, and Nathan Frey · 2024
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Fast and accurate protein structure search with foldseek
Michel Van Kempen, Stephanie S Kim, Charlotte Tumescheit, Milot Mirdita, Jeongjae Lee, Cameron LM Gilchrist, Johannes Söding, and Martin Steinegger · 2024
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Diffusion language models are versatile protein learners
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, and Quanquan Gu · 2024
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Improved motif-scaffolding with se (3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu, Andrew YK Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S Veeling, Frank Noé, et al · 2024
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Antigen-specific antibody design via direct energy-based preference optimization
Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, and Quanquan Gu · 2024
Closest in time.