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We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Normal distribution on the rotation group SO(3)
Dmitry I Nikolayev and Tatjana I Savyolov · 1970
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Tm-align: a protein structure alignment algorithm based on the tm-score
Yang Zhang and Jeffrey Skolnick · 2005
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MaxCluster: a tool for protein structure comparison and clustering
Alex Herbert and MJE Sternberg · 2008
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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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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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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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
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Protein structure generation via folding diffusion
Kevin Eric Wu, Kevin K Yang, Rianne van den Berg, James Zou, Alex Xijie Lu, and Ava P Amini · 2022
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Scope: improvements to the structural classification of proteins–extended database to facilitate variant interpretation and machine learning
John-Marc Chandonia, Lindsey Guan, Shiangyi Lin, Changhua Yu, Naomi K Fox, and Steven E Brenner · 2022
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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.
Foldseek: fast and accurate protein structure search
Michel van Kempen, Stephanie Kim, Charlotte Tumescheit, Milot Mirdita, Johannes Söding, and Martin Steinegger · 2022
Cited alongside, same era.
Se (3) diffusion model with application to protein backbone generation
Multisample flow matching: Straightening flows with minibatch couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, and Ricky Chen · 2023
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Se(3)-stochastic flow matching for protein backbone generation, 2023
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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Yeqing Lin and Mohammed AlQuraishi · 2023
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Analog bits: Generating discrete data using diffusion models with self-conditioning
Ting Chen, Ruixiang Zhang, and Geoffrey Hinton · 2023
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Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2023
Cited alongside, same era.
Riemannian flow matching on general geometries
Ricky TQ Chen and Yaron Lipman · 2023
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Leon Klein, Andreas Krämer, and Frank Noé · 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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Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, and Alexander Rives · 2023
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Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Kilian Fatras, Guy Wolf, and Yoshua Bengio · 2023
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