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Proteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life.
Stereochemistry of polypeptide chain configurations
G. N. Ramachandran, C. Ramakrishnan, and V. Sasisekharan · 1963
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Animating rotation with quaternion curves
Ken Shoemake · 1985
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The Protein Data Bank
Helen M. Berman, John Westbrook, Zukang Feng, Gary Gilliland, T. N. Bhat, Helge Weissig, Ilya N. Shindyalov, and Philip E. Bourne · 2000
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Native protein sequences are close to optimal for their structures
Brian Kuhlman and David Baker · 2000
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Protein structure prediction using rosetta
Carol A Rohl, Charlie EM Strauss, Kira MS Misura, and David Baker · 2004
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Scientific benchmarks for guiding macromolecular energy function improvement
Andrew Leaver-Fay, Matthew J. O’Meara, Mike Tyka, Ron Jacak, Yifan Song, Elizabeth H. Kellogg, James Thompson, Ian W. Davis, Roland A. Pache, Sergey Lyskov, Jeffrey J. Gray, Tanja Kortemme, Jane S. Richardson, James J. Havranek, Jack Snoeyink, David Baker, and Brian Kuhlman · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric A Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Cath: an expanded resource to predict protein function through structure and sequence
Natalie L Dawson, Tony E Lewis, Sayoni Das, Jonathan G Lees, David Lee, Paul Ashford, Christine A Orengo, and Ian Sillitoe · 2016
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Sgdr: Stochastic gradient descent with warm restarts, 2016
Ilya Loshchilov and Frank Hutter · 2016
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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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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 · 2017
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Prediction of amino acid side chain conformation using a deep neural network
Ke Liu, Xiangyan Sun, Jun Ma, Zhenyu Zhou, Qilin Dong, Shengwen Peng, Junqiu Wu, Suocheng Tan, Günter Blobel, and Jie Fan · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Generative modeling for protein structures
Namrata Anand and Possu Huang · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2018
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 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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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 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, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Protein sequence design with a learned potential
Namrata Anand, Raphael Eguchi, Irimpan I. Mathews, Carla P. Perez, Alexander Derry, Russ B. Altman, and Po-Ssu Huang · 2022
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Guided generative protein design using regularized transformers
Egbert Castro, Abhinav Godavarthi, Julian Rubinfien, Kevin B Givechian, Dhananjay Bhaskar, and Smita Krishnaswamy · 2022
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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
Cited alongside, same era.
Energy-based models for atomic-resolution protein conformations
Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, and Alexander Rives · 2020
Cited alongside, same era.
Ig-vae: Generative modeling of immunoglobulin proteins by direct 3d coordinate generation
Raphael R. Eguchi, Namrata Anand, Christian A. Choe, and Po-Ssu Huang · 2020
Cited alongside, same era.
Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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
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Fast and flexible protein design using deep graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M Kim · 2020
Cited alongside, same era.
Improved antibody structure prediction by deep learning of side chain conformations
Deniz Akpinaroglu, Jeffrey A Ruffolo, Sai Pooja Mahajan, and Jeffrey J Gray · 2021
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Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet · 2022
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Towards controllable protein design with conditional transformers
Noelia Ferruz and Birte Höcker · 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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Alphadesign: A graph protein design method and benchmark on alphafolddb
Zhangyang Gao, Cheng Tan, Stan Li, 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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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Regina Barzilay, and Tommi S. Jaakkola · 2022
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Attnpacker: An end-to-end deep learning method for rotamer-free protein side-chain packing
Matthew McPartlon and Jinbo Xu · 2022
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Deep generative modeling for protein design
Alexey Strokach and Philip M Kim · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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