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Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein design.
Design by directed evolution
Frances H Arnold · 1998
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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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Scoring function for automated assessment of protein structure template quality
Yang Zhang and Jeffrey Skolnick · 2004
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Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Sidhartha Chaudhury, Sergey Lyskov, and Jeffrey J Gray · 2010
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Prediction of aggregation prone regions of therapeutic proteins
Naresh Chennamsetty, Vladimir Voynov, Veysel Kayser, Bernhard Helk, and Bernhardt L Trout · 2010
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Rosetta3: an object-oriented software suite for the simulation and design of macromolecules
Andrew Leaver-Fay, Michael Tyka, Steven M Lewis, Oliver F Lange, James Thompson, Ron Jacak, Kristian W Kaufman, P Douglas Renfrew, Colin A Smith, Will Sheffler, et al · 2011
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Deep mutational scanning: a new style of protein science
Douglas M Fowler and Stanley Fields · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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The camsol method of rational design of protein mutants with enhanced solubility
Pietro Sormanni, Francesco A Aprile, and Michele Vendruscolo · 2015
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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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Mutation effects predicted from sequence co-variation
Thomas A Hopf, John B Ingraham, Frank J Poelwijk, Charlotta PI Schärfe, Michael Springer, Chris Sander, and Debora S Marks · 2017
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Mutational landscape of antibody variable domains reveals a switch modulating the interdomain conformational dynamics and antigen binding
Patrick Koenig, Chingwei V Lee, Benjamin T Walters, Vasantharajan Janakiraman, Jeremy Stinson, Thomas W Patapoff, and Germaine Fuh · 2017
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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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Engineering highly functional thermostable proteins using ancestral sequence reconstruction
Yosephin Gumulya, Jong-Min Baek, Shun-Jie Wun, Raine ES Thomson, Kurt L Harris, Dominic JB Hunter, James BYH Behrendorff, Justyna Kulig, Shan Zheng, Xueming Wu, et al · 2018
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Gene3d: extensive prediction of globular domains in proteins
Tony E Lewis, Ian Sillitoe, Natalie Dawson, Su Datt Lam, Tristan Clarke, David Lee, Christine Orengo, and Jonathan Lees · 2018
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Deep generative models of genetic variation capture the effects of mutations
Adam J Riesselman, John B Ingraham, and Debora S Marks · 2018
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Clustering huge protein sequence sets in linear time
Martin Steinegger and Johannes Söding · 2018
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Generative models for graph-based protein design
John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola · 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
Cited alongside, same era.
Five computational developability guidelines for therapeutic antibody profiling
Matthew IJ Raybould, Claire Marks, Konrad Krawczyk, Bruck Taddese, Jaroslaw Nowak, Alan P Lewis, Alexander Bujotzek, Jiye Shi, and Charlotte M Deane · 2019
Cited alongside, same era.
Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
Cited alongside, same era.
Optimizing antibody affinity and stability by the automated design of the variable light-heavy chain interfaces
Shira Warszawski, Aliza Borenstein Katz, Rosalie Lipsh, Lev Khmelnitsky, Gili Ben Nissan, Gabriel Javitt, Orly Dym, Tamar Unger, Orli Knop, Shira Albeck, et al · 2019
Cited alongside, same era.
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
Generative language modeling for antibody design
Richard W Shuai, Jeffrey A Ruffolo, and Jeffrey J Gray · 2021
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Cath: increased structural coverage of functional space
Ian Sillitoe, Nicola Bordin, Natalie Dawson, Vaishali P Waman, Paul Ashford, Harry M Scholes, Camilla SM Pang, Laurel Woodridge, Clemens Rauer, Neeladri Sen, et al · 2021
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki · 2021
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Proteinbert: A universal deep-learning model of protein sequence and function
Nadav Brandes, Dan Ofer, Yam Peleg, Nadav Rappoport, and Michal Linial · 2022
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Cited alongside, same era.
Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al · 2020
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael JL Townshend, and Ron Dror · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Progen: Language modeling for protein generation
Ali Madani, Bryan McCann, Nikhil Naik, Nitish Shirish Keskar, Namrata Anand, Raphael R Eguchi, Po-Ssu Huang, and Richard Socher · 2020
Cited alongside, same era.
An evolution-based model for designing chorismate mutase enzymes
William P Russ, Matteo Figliuzzi, Christian Stocker, Pierre Barrat-Charlaix, Michael Socolich, Peter Kast, Donald Hilvert, Remi Monasson, Simona Cocco, Martin Weigt, et al · 2020
Cited alongside, same era.
Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
J Dauparas, I Anishchenko, N Bennett, H Bai, RJ Ragotte, LF Milles, BIM Wicky, A Courbet, RJ de Haas, N Bethel, et al · 2022
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A deep unsupervised language model for protein design
Noelia Ferruz, Steffen Schmidt, and Birte Höcker · 2022
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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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Efficient evolution of human antibodies from general protein language models and sequence information alone
Brian L Hie, Duo Xu, Varun R Shanker, Theodora UJ Bruun, Payton A Weidenbacher, Shaogeng Tang, and Peter S Kim · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 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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Deciphering the language of antibodies using self-supervised learning
Jinwoo Leem, Laura S Mitchell, James HR Farmery, Justin Barton, and Jacob D Galson · 2022
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A conversational paradigm for program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
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Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan Gomez, Debora S Marks, and Yarin Gal · 2022
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Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Jeffrey A Ruffolo, Lee-Shin Chu, Sai Pooja Mahajan, and Jeffrey J Gray · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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Foldseek: fast and accurate protein structure search
Michel van Kempen, Stephanie Kim, Charlotte Tumescheit, Milot Mirdita, Johannes Söding, and Martin Steinegger · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
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Non-identifiability and the blessings of misspecification in models of molecular fitness and phylogeny
Eli N Weinstein, Alan N Amin, Jonathan Frazer, and Debora S Marks · 2022
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Hallucinating protein assemblies
Basile IM Wicky, Lukas F Milles, Alexis Courbet, Robert J Ragotte, Justas Dauparas, Elias Kinfu, Sam Tipps, Ryan D Kibler, Minkyung Baek, Frank DiMaio, et al · 2022
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Convolutions are competitive with transformers for protein sequence pretraining
Kevin K Yang, Alex X Lu, and Nicolo K Fusi · 2022
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