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Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm.
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Francis Crick · 1970
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Christian B Anfinsen · 1973
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Roy Saffhill and JJ Weiss · 1973
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Cyrus Chothia · 1992
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Clonal selection and learning in the antibody system
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Uniprotkb/swiss-prot: the manually annotated section of the uniprot knowledgebase
Emmanuel Boutet, Damien Lieberherr, Michael Tognolli, Michel Schneider, and Amos Bairoch · 2007
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Exploring the structure and function paradigm
Oliver C Redfern, Benoit Dessailly, and Christine A Orengo · 2008
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Origin and evolution of the genetic code: the universal enigma
Eugene V Koonin and Artem S Novozhilov · 2009
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Philip A Romero and Frances H Arnold · 2009
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A new generation of crystallographic validation tools for the protein data bank
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Principles of early drug discovery
James P Hughes, Stephen Rees, S Barrett Kalindjian, and Karen L Philpott · 2011
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Outcome of the first electron microscopy validation task force meeting
Richard Henderson, Andrej Sali, Matthew L Baker, Bridget Carragher, Batsal Devkota, Kenneth H Downing, Edward H Egelman, Zukang Feng, Joachim Frank, Nikolaus Grigorieff, et al · 2012
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The protein-folding problem, 50 years on
Ken A Dill and Justin L MacCallum · 2012
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Hhblits: lightning-fast iterative protein sequence searching by hmm-hmm alignment
Michael Remmert, Andreas Biegert, Andreas Hauser, and Johannes Söding · 2012
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The ncbi taxonomy database
Scott Federhen · 2012
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Engineering the third wave of biocatalysis
Uwe T Bornscheuer, GW Huisman, RJ Kazlauskas, S Lutz, JC Moore, and K Robins · 2012
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Pubmed: the bibliographic database
Kathi Canese and Sarah Weis · 2013
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Recommendations of the wwpdb nmr validation task force
Gaetano T Montelione, Michael Nilges, Ad Bax, Peter Güntert, Torsten Herrmann, Jane S Richardson, Charles D Schwieters, Wim F Vranken, Geerten W Vuister, David S Wishart, et al · 2013
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Deep mutational scanning: a new style of protein science
Douglas M Fowler and Stanley Fields · 2014
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Sabdab: the structural antibody database
James Dunbar, Konrad Krawczyk, Jinwoo Leem, Terry Baker, Angelika Fuchs, Guy Georges, Jiye Shi, and Charlotte M Deane · 2014
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Interproscan 5: genome-scale protein function classification
Philip Jones, David Binns, Hsin-Yu Chang, Matthew Fraser, Weizhong Li, Craig McAnulla, Hamish McWilliam, John Maslen, Alex Mitchell, Gift Nuka, et al · 2014
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Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Baris E Suzek, Yuqi Wang, Hongzhan Huang, Peter B McGarvey, Cathy H Wu, and UniProt Consortium · 2015
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Methods for the directed evolution of proteins
Michael S Packer and David R Liu · 2015
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Enzymes: principles and biotechnological applications
Peter K Robinson · 2015
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Dissecting enzyme function with microfluidic-based deep mutational scanning
Philip A Romero, Tuan M Tran, and Adam R Abate · 2015
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Bindingdb in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology
Michael K Gilson, Tiqing Liu, Michael Baitaluk, George Nicola, Linda Hwang, and Jenny Chong · 2016
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 2017
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Uniclust databases of clustered and deeply annotated protein sequences and alignments
Milot Mirdita, Lars Von Den Driesch, Clovis Galiez, Maria J Martin, Johannes Söding, and Martin Steinegger · 2017
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The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew J O’Meara, Frank P DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram K Mulligan, Kalli Kappel, et al · 2017
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires
Aleksandr Kovaltsuk, Jinwoo Leem, Sebastian Kelm, James Snowden, Charlotte M Deane, and Konrad Krawczyk · 2018
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Consensus coding sequence (ccds) database: a standardized set of human and mouse protein-coding regions supported by expert curation
Shashikant Pujar, Nuala A O’Leary, Catherine M Farrell, Jane E Loveland, Jonathan M Mudge, Craig Wallin, Carlos G Girón, Mark Diekhans, If Barnes, Ruth Bennett, et al · 2018
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Deepsf: deep convolutional neural network for mapping protein sequences to folds
Jie Hou, Badri Adhikari, and Jianlin Cheng · 2018
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Directed evolution: bringing new chemistry to life
Frances H Arnold · 2018
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Nucleic acids research
Protein data bank: the single global archive for 3d macromolecular structure data · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
Martin Steinegger, Milot Mirdita, and Johannes Söding · 2019
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Albert: A lite bert for self-supervised learning of language representations
Z Lan · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang · 2019
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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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String v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
Damian Szklarczyk, Annika L Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, Nadezhda T Doncheva, John H Morris, Peer Bork, et al · 2019
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Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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Protein structure prediction beyond alphafold
Guo-Wei Wei · 2019
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Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda · 2019
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Netgo: improving large-scale protein function prediction with massive network information
Ronghui You, Shuwei Yao, Yi Xiong, Xiaodi Huang, Fengzhu Sun, Hiroshi Mamitsuka, and Shanfeng Zhu · 2019
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Deepgoplus: improved protein function prediction from sequence
Maxat Kulmanov and Robert Hoehndorf · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 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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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Electra: Pre-training text encoders as discriminators rather than generators
K Clark · 2020
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Transformer protein language models are unsupervised structure learners
Roshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov, and Alexander Rives · 2020
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Zinc20—a free ultralarge-scale chemical database for ligand discovery
John J Irwin, Khanh G Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R Wong, Munkhzul Khurelbaatar, Yurii S Moroz, John Mayfield, and Roger A Sayle · 2020
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Learning the protein language: Evolution, structure, and function
Tristan Bepler and Bonnie Berger · 2021
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The language of proteins: Nlp, machine learning & protein sequences
Dan Ofer, Nadav Brandes, and Michal Linial · 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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Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N Kinch, R Dustin Schaeffer, et al · 2021
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Msa transformer
Roshan M Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, and Alexander Rives · 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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Flip: Benchmark tasks in fitness landscape inference for proteins
Christian Dallago, Jody Mou, Kadina E Johnston, Bruce J Wittmann, Nicholas Bhattacharya, Samuel Goldman, Ali Madani, and Kevin K Yang · 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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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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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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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, 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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Glm: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang · 2021
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Deciphering antibody affinity maturation with language models and weakly supervised learning
Jeffrey A Ruffolo, Jeffrey J Gray, and Jeremias Sulam · 2021
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Pfam: The protein families database in 2021
Jaina Mistry, Sara Chuguransky, Lowri Williams, Matloob Qureshi, Gustavo A Salazar, Erik LL Sonnhammer, Silvio CE Tosatto, Lisanna Paladin, Shriya Raj, Lorna J Richardson, et al · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
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Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2021
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The human phenotype ontology in 2021
Sebastian Köhler, Michael Gargano, Nicolas Matentzoglu, Leigh C Carmody, David Lewis-Smith, Nicole A Vasilevsky, Daniel Danis, Ganna Balagura, Gareth Baynam, Amy M Brower, et al · 2021
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Netgo 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information
Shuwei Yao, Ronghui You, Shaojun Wang, Yi Xiong, Xiaodi Huang, and Shanfeng Zhu · 2021
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Generating novel protein sequences using gibbs sampling of masked language models
Sean R Johnson, Sarah Monaco, Kenneth Massie, and Zaid Syed · 2021
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Animal immunization, in vitro display technologies, and machine learning for antibody discovery
Andreas H Laustsen, Victor Greiff, Aneesh Karatt-Vellatt, Serge Muyldermans, and Timothy P Jenkins · 2021
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Protein–dna/rna interactions: an overview of investigation methods in the-omics era
Flora Cozzolino, Ilaria Iacobucci, Vittoria Monaco, and Maria Monti · 2021
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Controllable protein design with language models
Noelia Ferruz and Birte Höcker · 2022
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Prediction of designer-recombinases for dna editing with generative deep learning
Lukas Theo Schmitt, Maciej Paszkowski-Rogacz, Florian Jug, and Frank Buchholz · 2022
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Ig-vae: Generative modeling of protein structure by direct 3d coordinate generation
Raphael R Eguchi, Christian A Choe, and Po-Ssu Huang · 2022
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Protgpt2 is a deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, and Birte Höcker · 2022
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Protein language models and structure prediction: Connection and progression
Bozhen Hu, Jun Xia, Jiangbin Zheng, Cheng Tan, Yufei Huang, Yongjie Xu, and Stan Z Li · 2022
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Learning functional properties of proteins with language models
Serbulent Unsal, Heval Atas, Muammer Albayrak, Kemal Turhan, Aybar C Acar, and Tunca Doğan · 2022
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Pifold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, Pablo Chacón, and Stan Z Li · 2022
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A survey on protein representation learning: Retrospect and prospect
Lirong Wu, Yufei Huang, Haitao Lin, and Stan Z Li · 2022
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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Lm-gvp: an extensible sequence and structure informed deep learning framework for protein property prediction
Zichen Wang, Steven A Combs, Ryan Brand, Miguel Romero Calvo, Panpan Xu, George Price, Nataliya Golovach, Emmanuel O Salawu, Colby J Wise, Sri Priya Ponnapalli, et al · 2022
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Colabfold: making protein folding accessible to all
Milot Mirdita, Konstantin Schütze, Yoshitaka Moriwaki, Lim Heo, Sergey Ovchinnikov, and Martin Steinegger · 2022
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Single-sequence protein structure prediction using supervised transformer protein language models
Wenkai Wang, Zhenling Peng, and Jianyi Yang · 2022
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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
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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
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Distilprotbert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts
Yaron Geffen, Yanay Ofran, and Ron Unger · 2022
Cited alongside, same era.
Rita: a study on scaling up generative protein sequence models
Daniel Hesslow, Niccoló Zanichelli, Pascal Notin, Iacopo Poli, and Debora Marks · 2022
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Deciphering the language of antibodies using self-supervised learning
Jinwoo Leem, Laura S Mitchell, James HR Farmery, Justin Barton, and Jacob D Galson · 2022
Cited alongside, same era.
Ablang: an antibody language model for completing antibody sequences
Tobias H Olsen, Iain H Moal, and Charlotte M Deane · 2022
Cited alongside, same era.
Soumya Ram and Tristan Bepler · 2022
Cited alongside, same era.
Empowering molecule discovery for molecule-caption translation with large language models: A chatgpt perspective
Jiatong Li, Yunqing Liu, Wenqi Fan, Xiao-Yong Wei, Hui Liu, Jiliang Tang, and Qing Li · 2024
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xtrimopglm: unified 100b-scale pre-trained transformer for deciphering the language of protein
Bo Chen, Xingyi Cheng, Pan Li, Yangli-ao Geng, Jing Gong, Shen Li, Zhilei Bei, Xu Tan, Boyan Wang, Xin Zeng, et al · 2024
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Prollama: A protein large language model for multi-task protein language processing
Liuzhenghao Lv, Zongying Lin, Hao Li, Yuyang Liu, Jiaxi Cui, Calvin Yu-Chian Chen, Li Yuan, and Yonghong Tian · 2024
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Proteinnpt: improving protein property prediction and design with non-parametric transformers
Pascal Notin, Ruben Weitzman, Debora Marks, and Yarin Gal · 2024
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Scientific large language models: A survey on biological & chemical domains
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Petribert: Augmenting bert with tridimensional encoding for inverse protein folding and design
Baldwin Dumortier, Antoine Liutkus, Clément Carré, and Gabriel Krouk · 2022
Cited alongside, same era.
Peer: a comprehensive and multi-task benchmark for protein sequence understanding
Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Ma Chang, Runcheng Liu, and Jian Tang · 2022
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.
Multi-level protein structure pre-training via prompt learning
Zeyuan Wang, Qiang Zhang, HU Shuang-Wei, Haoran Yu, Xurui Jin, Zhichen Gong, and Huajun Chen · 2022
Cited alongside, same era.
Zymctrl: a conditional language model for the controllable generation of artificial enzymes
Geraldene Munsamy, Sebastian Lindner, Philipp Lorenz, and Noelia Ferruz · 2022
Cited alongside, same era.
Ontoprotein: Protein pretraining with gene ontology embedding
Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Jiazhang Lian, Qiang Zhang, and Huajun Chen · 2022
Cited alongside, same era.
Protein representation learning via knowledge enhanced primary structure reasoning
Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Bian Cheng, and Yizhou Yu · 2022
Cited alongside, same era.
Qiang Zhang, Keyang Ding, Tianwen Lyv, Xinda Wang, Qingyu Yin, Yiwen Zhang, Jing Yu, Yuhao Wang, Xiaotong Li, Zhuoyi Xiang, et al · 2024
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Designing proteins with language models
Jeffrey A Ruffolo and Ali Madani · 2024
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Convolutions are competitive with transformers for protein sequence pretraining
Kevin K Yang, Nicolo Fusi, and Alex X Lu · 2024
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Protein–dna binding sites prediction based on pre-trained protein language model and contrastive learning
Yufan Liu and Boxue Tian · 2024
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Genome-scale annotation of protein binding sites via language model and geometric deep learning
Qianmu Yuan, Chong Tian, and Yuedong Yang · 2024
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Protchatgpt: Towards understanding proteins with large language models
Chao Wang, Hehe Fan, Ruijie Quan, and Yi Yang · 2024
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Prot2text: Multimodal protein’s function generation with gnns and transformers
Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, and Michalis Vazirgiannis · 2024
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Machine learning for functional protein design
Pascal Notin, Nathan Rollins, Yarin Gal, Chris Sander, and Debora Marks · 2024
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De novo protein design—from new structures to programmable functions
Tanja Kortemme · 2024
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Graph machine learning in the era of large language models (llms)
Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, et al · 2024
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Moleculargpt: Open large language model (llm) for few-shot molecular property prediction
Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, and Qiaoyu Tan · 2024
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Leveraging biomolecule and natural language through multi-modal learning: A survey
Qizhi Pei, Lijun Wu, Kaiyuan Gao, Jinhua Zhu, Yue Wang, Zun Wang, Tao Qin, and Rui Yan · 2024
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A survey on rag meeting llms: Towards retrieval-augmented large language models
Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li · 2024
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ESM Cambrian: Revealing the mysteries of proteins with unsupervised learning
ESM Team · 2024
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Long-context protein language model
Yingheng Wang, Zichen Wang, Gil Sadeh, Luca Zancato, Alessandro Achille, George Karypis, and Huzefa Rangwala · 2024
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Haohao Qu, Liangbo Ning, Rui An, Wenqi Fan, Tyler Derr, Xin Xu, and Qing Li · 2024
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Ssd4rec: a structured state space duality model for efficient sequential recommendation
Haohao Qu, Yifeng Zhang, Liangbo Ning, Wenqi Fan, and Qing Li · 2024
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Mixture of experts enable efficient and effective protein understanding and design
Ning Sun, Shuxian Zou, Tianhua Tao, Sazan Mahbub, Dian Li, Yonghao Zhuang, Hongyi Wang, Xingyi Cheng, Le Song, and Eric P Xing · 2024
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Le Song, Eran Segal, and Eric Xing · 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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p-iggen: A paired antibody generative language model
Oliver Marcus Turnbull, Dino Oglic, Rebecca Croasdale-Wood, and Charlotte M Deane · 2024
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Msagpt: Neural prompting protein structure prediction via msa generative pre-training
Bo Chen, Zhilei Bei, Xingyi Cheng, Pan Li, Jie Tang, and Le Song · 2024
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Poet: A generative model of protein families as sequences-of-sequences
Timothy Truong Jr and Tristan Bepler · 2024
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Protmamba: a homology-aware but alignment-free protein state space model
Damiano Sgarbossa, Cyril Malbranke, and Anne-Florence Bitbol · 2024
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Codon language embeddings provide strong signals for use in protein engineering
Carlos Outeiral and Charlotte M Deane · 2024
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Ptm-mamba: A ptm-aware protein language model with bidirectional gated mamba blocks
Zhangzhi Peng, Benjamin Schussheim, and Pranam Chatterjee · 2024
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Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al · 2024
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Openproteinset: Training data for structural biology at scale
Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan, Lukas Jarosch, Dan Berenberg, Ian Fisk, Andrew Watkins, Stephen Ra, Richard Bonneau, and Mohammed AlQuraishi · 2024
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Structure-informed protein language model
Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie Lozano, Payel Das, and Jian Tang · 2024
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Biophysics-based protein language models for protein engineering
Sam Gelman, Bryce Johnson, Chase Freschlin, Sameer D’Costa, Anthony Gitter, and Philip A Romero · 2024
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Endowing protein language models with structural knowledge
Dexiong Chen, Philip Hartout, Paolo Pellizzoni, Carlos Oliver, and Karsten Borgwardt · 2024
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Adapting protein language models for structure-conditioned design
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Proteingym: Large-scale benchmarks for protein fitness prediction and design
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Protein language models are performant in structure-free virtual screening
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Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites
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Prollm: Protein chain-of-thoughts enhanced llm for protein-protein interaction prediction
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Prot2token: A multi-task framework for protein language processing using autoregressive language modeling
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Prott3: Protein-to-text generation for text-based protein understanding
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Integrating genetic algorithms and language models for enhanced enzyme design
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A general temperature-guided language model to design proteins of enhanced stability and activity
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Instructplm: Aligning protein language models to follow protein structure instructions
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Conditional language models enable the efficient design of proficient enzymes
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Protein language models learn evolutionary statistics of interacting sequence motifs
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Interplm: Discovering interpretable features in protein language models via sparse autoencoders
Elana Simon and James Zou · 2024
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Augmenting large language models with chemistry tools
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Are protein language models compute optimal?
Yaiza Serrano, Álvaro Ciudad, and Alexis Molina · 2024
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Fred Zhangzhi Peng, Pranam Chatterjee, and contributors · 2024
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Simulating 500 million years of evolution with a language model
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Decoding the molecular language of proteins with evolla
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