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Three-dimensional native states of natural proteins display recurring and hierarchical patterns.
Multidimensional binary search trees used for associative searching
Jon Louis Bentley · 1975
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Robust estimation of a location parameter
Peter J Huber · 1992
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Prediction of local structure in proteins using a library of sequence-structure motifs
Christopher Bystroff and David Baker · 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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Relevance of rotationally equivariant convolutions for predicting molecular properties
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, and Frank Noé · 2008
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Modularity of protein folds as a tool for template-free modeling of structures
Brinda Vallat, Carlos Madrid-Aliste, and Andras Fiser · 2015
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Layer normalization, 2016
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Pdbflex: exploring flexibility in protein structures
Thomas Hrabe, Zhanwen Li, Mayya Sedova, Piotr Rotkiewicz, Lukasz Jaroszewski, and Adam Godzik · 2016
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Coarse-grained protein models and their applications
Sebastian Kmiecik, Dominik Gront, Michal Kolinski, Lukasz Wieteska, Aleksandra Elzbieta Dawid, and Andrzej Kolinski · 2016
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Tertiary alphabet for the observable protein structural universe
Craig O. Mackenzie, Jianfu Zhou, and Gevorg Grigoryan · 2016
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning, 2017
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2017
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Learning hierarchical motif embeddings for protein engineering
Thrasyvoulos Karydis · 2017
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Protein structural motifs in prediction and design
Craig O Mackenzie and Gevorg Grigoryan · 2017
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Deepsf: deep convolutional neural network for mapping protein sequences to folds
Jie Hou, Badri Adhikari, and Jianlin Cheng · 2018
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds, 2018
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Christoph Wehmeyer and Frank Noé · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data, 2018
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
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Deepconv-dti: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Ingoo Lee, Jongsoo Keum, and Hojung Nam · 2019
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Coarse-graining auto-encoders for molecular dynamics
Wujie Wang and Rafael Gómez-Bombarelli · 2019
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Ig-VAE: Generative modeling of protein structure by direct 3d coordinate generation
Raphael R. Eguchi, Christian A. Choe, and Po-Ssu Huang · 2020
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Se(3)-transformers: 3d roto-translation equivariant attention networks, 2020
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, and Max Welling · 2020
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Haiku: Sonnet for JAX
Tom Hennigan, Trevor Cai, Tamara Norman, Lena Martens, and Igor Babuschkin · 2020
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Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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A compact review of molecular property prediction with graph neural networks
Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer · 2020
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic · 2021
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Euclidean transformers for macromolecular structures: Lessons learned
David D Liu, Ligia Melo, Allan Costa, Martin Vögele, Raphael JL Townshend, and Ron O Dror · 2022
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Progressive distillation for fast sampling of diffusion models, 2022
Tim Salimans and Jonathan Ho · 2022
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E(n) equivariant graph neural networks, 2022
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2022
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Tertiary motifs as building blocks for the design of protein-binding peptides
Sebastian Swanson, Venkatesh Sivaraman, Gevorg Grigoryan, and Amy E Keating · 2022
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Atom3d: Tasks on molecules in three dimensions, 2022
Raphael J. L. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, Risi Kondor, Russ B. Altman, and Ron O. Dror · 2022
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Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas Guibas · 2021
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Learning from protein structure with geometric vector perceptrons, 2021
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J. L. Townshend, and Ron Dror · 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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Sidechainnet: An all-atom protein structure dataset for machine learning
Jonathan Edward King and David Ryan Koes · 2021
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Deep generative models create new and diverse protein structures
Zeming Lin, Tom Sercu, Yann LeCun, and Alexander Rives · 2021
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Deep learning protein conformational space with convolutions and latent interpolations
Venkata K Ramaswamy, Samuel C Musson, Chris G Willcocks, and Matteo T Degiacomi · 2021
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Euclidean symmetry and equivariance in machine learning
Tess E Smidt · 2021
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Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, and Rafael Gómez-Bombarelli · 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, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker · 2022
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High-resolution de novo structure prediction from primary sequence
Ruidong Wu, Fan Ding, Rui Wang, Rui Shen, Xiwen Zhang, Shitong Luo, Chenpeng Su, Zuofan Wu, Qi Xie, Bonnie Berger, et al · 2022
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Convolutions are competitive with transformers for protein sequence pretraining
Kevin K Yang, Nicolo Fusi, and Alex X Lu · 2022
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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Ankh: Optimized protein language model unlocks general-purpose modelling, 2023
Ahmed Elnaggar, Hazem Essam, Wafaa Salah-Eldin, Walid Moustafa, Mohamed Elkerdawy, Charlotte Rochereau, and Burkhard Rost · 2023
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A latent diffusion model for protein structure generation, 2023
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Yi-Lun Liao and Tess Smidt · 2023
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Generating novel, designable, and diverse protein structures by equivariantly diffusing oriented residue clouds, 2023
Yeqing Lin and Mohammed AlQuraishi · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
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Protein ensemble generation through variational autoencoder latent space sampling
Sanaa Mansoor, Minkyung Baek, Hahnbeom Park, Gyu Rie Lee, and David Baker · 2023
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem, 2023
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Holographic-(v)ae: an end-to-end so(3)-equivariant (variational) autoencoder in fourier space, 2023
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De novo design of protein structure and function with rfdiffusion
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Chemically transferable generative backmapping of coarse-grained proteins, 2023
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