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Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function.
Implications of protein flexibility for drug discovery
Simon J Teague · 2003
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Protein structure quality assessment based on the distance profiles of consecutive backbone c α \alpha atoms
Sandeep Chakraborty, Ravindra Venkatramani, Basuthkar J Rao, Bjarni Asgeirsson, and Abhaya M Dandekar · 2013
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From molecular dynamics to brownian dynamics
Radek Erban · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Intrinsically disordered proteins in cellular signalling and regulation
Peter E Wright and H Jane Dyson · 2015
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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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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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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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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 · 2021
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
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Alphafold2 fails to predict protein fold switching
Devlina Chakravarty and Lauren L Porter · 2022
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola · 2022
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Sampling alternative conformational states of transporters and receptors with alphafold2
Diego Del Alamo, Davide Sala, Hassane S Mchaourab, and Jens Meiler · 2022
End-to-end protein-ligand complex structure generation with diffusion-based generative models
Shuya Nakata, Yoshiharu Mori, and Shigenori Tanaka · 2022
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Dynamic-backbone protein-ligand structure prediction with multiscale generative diffusion models
Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F Miller III, and Anima Anandkumar · 2022
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Impact of protein conformational diversity on alphafold predictions
Tadeo Saldaño, Nahuel Escobedo, Julia Marchetti, Diego Javier Zea, Juan Mac Donagh, Ana Julia Velez Rueda, Eduardo Gonik, Agustina García Melani, Julieta Novomisky Nechcoff, Martín N Salas, et al · 2022
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Structure-based drug design with equivariant diffusion models
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, et al · 2022
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Speach_af: Sampling protein ensembles and conformational heterogeneity with alphafold2
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e3nn: Euclidean neural networks
Mario Geiger and Tess Smidt · 2022
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Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
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Illuminating protein space with a programmable generative model
John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, et al · 2022
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola · 2022
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, and Alexander Rives · 2022
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A score-based geometric model for molecular dynamics simulations
Fang Wu, Qiang Zhang, Xurui Jin, Yinghui Jiang, and Stan Z Li
Cited in the paper.
Protein structure generation via folding diffusion
Kevin E Wu, Kevin K Yang, Rianne van den Berg, James Y Zou, Alex X Lu, and Ava P Amini
Cited in the paper.
Richard A Stein and Hassane S Mchaourab · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
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 · 2022
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Prediction of multiple conformational states by combining sequence clustering with alphafold2
Hannah K Wayment-Steele, Sergey Ovchinnikov, Lucy Colwell, and Dorothee Kern · 2022
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Protein structure prediction has reached the single-structure frontier
Thomas J Lane · 2023
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