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Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around.
Normal distribution on the rotation group SO(3)
Dmitry I Nikolayev and Tatjana I Savyolov · 1970
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Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Wolfgang Kabsch and Christian Sander · 1983
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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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MaxCluster: a tool for protein structure comparison and clustering
Alex Herbert and MJE Sternberg · 2008
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De novo computational design of retro-aldol enzymes
Lin Jiang, Eric A Althoff, Fernando R Clemente, Lindsey Doyle, Daniela Rothlisberger, Alexandre Zanghellini, Jasmine L Gallaher, Jamie L Betker, Fujie Tanaka, Carlos F Barbas III, Donald Hilvert, Kendal N Houk, Barry L. Stoddard, and David Baker · 2008
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Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction
Justin B Siegel, Alexandre Zanghellini, Helena M Lovick, Gert Kiss, Abigail R Lambert, Jennifer L StClair, Jasmine L Gallaher, Donald Hilvert, Michael H Gelb, Barry L Stoddard, Kendall N Houk, Forrest E Michael, and David Baker · 2010
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Proof of principle for epitope-focused vaccine design
Bruno E Correia, John T Bates, Rebecca J Loomis, Gretchen Baneyx, Chris Carrico, Joseph G Jardine, Peter Rupert, Colin Correnti, Oleksandr Kalyuzhniy, Vinayak Vittal, Mary J Connell, Eric Stevens, Alexandria Schroeter, Man Chen, Skye Macpherson, Andreia M Serra, Yumiko Adachi, Margaret A Holmes, Yuxing Li, Rachel E Klevit, Barney S Graham, Richard T Wyatt, David Baker, Roland K Strong, James E Crowe, Jr, Philip R Johnson, and William R Schief · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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A computationally designed inhibitor of an Epstein-Barr viral BCL-2 protein induces apoptosis in infected cells
Erik Procko, Geoffrey Y Berguig, Betty W Shen, Yifan Song, Shani Frayo, Anthony J Convertine, Daciana Margineantu, Garrett Booth, Bruno E Correia, Yuanhua Cheng, William R Schief, David M Hockenbery, Oliver W Press, Barry L Stoddard, Patrick S Stayton, and David Baker · 2014
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Applied Stochastic Differential Equations
Simo Särkkä and Arno Solin · 2019
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Machine-learning-guided directed evolution for protein engineering
Kevin K Yang, Zachary Wu, and Frances H Arnold · 2019
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Equivariant flows: exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 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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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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Solving inverse problems in medical imaging with score-based generative models
Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon · 2021
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Deep learning methods for designing proteins scaffolding functional sites
Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Ivan Anishchenko, Minkyung Baek, Joseph L Watson, Jung Ho Chun, Lukas F Milles, Justas Dauparas, Marc Exposit, Wei Yang, Amijai Saragovi, Sergey Ovchinnikov, and David A. Baker · 2021
Cited alongside, same era.
Diffusion posterior sampling for general noisy inverse problems
Improving de novo protein binder design with deep learning
Nathaniel R Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, et al · 2023
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Se (3)-stochastic flow matching for protein backbone generation
Avishek Joey Bose, Tara Akhound-Sadegh, Kilian Fatras, Guillaume Huguet, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, and Alexander Tong · 2023
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Riemannian Flow Matching on General Geometries, February 2023
Ricky T. Q. Chen and Yaron Lipman · 2023
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Quan Dao, Hao Phung, Binh Nguyen, and Anh Tran · 2023
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Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye · 2022
Cited alongside, same era.
Robust deep learning-based protein sequence design using ProteinMPNN
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, and D. Baker · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Video diffusion models, 2022
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet · 2022
Cited alongside, same era.
Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
Cited alongside, same era.
Pseudoinverse-guided diffusion models for inverse problems
Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz · 2022
Cited alongside, same era.
Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Kieran Didi, Francisco Vargas, Simon V Mathis, Vincent Dutordoir, Emile Mathieu, Urszula J Komorowska, and Pietro Lio · 2023
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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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Leon Klein, Andreas Krämer, and Frank Noé · 2023
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Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2023
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Training-free linear image inversion via flows
Ashwini Pokle, Matthew J Muckley, Ricky TQ Chen, and Brian Karrer · 2023
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On kinetic optimal probability paths for generative models
Neta Shaul, Ricky TQ Chen, Maximilian Nickel, Matthew Le, and Yaron Lipman · 2023
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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
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Practical and asymptotically exact conditional sampling in diffusion models
Luhuan Wu, Brian L Trippe, Christian A Naesseth, David M Blei, and John P Cunningham · 2023
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Guided flows for generative modeling and decision making
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, and Ricky TQ Chen · 2023
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