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Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.
P-SEA: a new efficient assignment of secondary structure from c α \alpha trace of proteins
Gilles Labesse, N Colloc’h, Joël Pothier, and J-P Mornon · 1997
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The PyMOL molecular graphics system
Warren L DeLano · 2002
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Generative modeling for protein structures
Namrata Anand and Possu Huang · 2018
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On the convergence of Adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar · 2018
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Protein materials engineering with dna
Janet R McMillan, Oliver G Hayes, Peter H Winegar, and Chad A Mirkin · 2019
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Fully differentiable full-atom protein backbone generation, 2019
Namrata Anand, Raphael Eguchi, and Po-Ssu Huang · 2019
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
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RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network
Sari Sabban and Mikhail Markovsky · 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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De novo protein design for novel folds using guided conditional Wasserstein generative adversarial networks
Mostafa Karimi, Shaowen Zhu, Yue Cao, and Yang Shen · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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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Improved protein structure prediction using predicted interresidue orientations
Jianyi Yang, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, and David Baker · 2020
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Learning ground states of quantum hamiltonians with graph networks
Dmitrii Kochkov, Tobias Pfaff, Alvaro Sanchez-Gonzalez, Peter Battaglia, and Bryan K Clark · 2021
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Protein sequence design with deep generative models
Zachary Wu, Kadina E Johnston, Frances H Arnold, and Kevin K Yang · 2021
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De novo protein design by deep network hallucination
Ivan Anishchenko, Samuel J Pellock, Tamuka M Chidyausiku, Theresa A Ramelot, Sergey Ovchinnikov, Jingzhou Hao, Khushboo Bafna, Christoffer Norn, Alex Kang, Asim K Bera, et al · 2021
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Expanding functional protein sequence spaces using generative adversarial networks
Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Irmantas Rokaitis, Jan Zrimec, Simona Poviloniene, Audrius Laurynenas, Sandra Viknander, Wissam Abuajwa, et al · 2021
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Generating functional protein variants with variational autoencoders
Alex Hawkins-Hooker, Florence Depardieu, Sebastien Baur, Guillaume Couairon, Arthur Chen, and David Bikard · 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
Cited alongside, same era.
GraphDF: A discrete flow model for molecular graph generation
Youzhi Luo, Keqiang Yan, and Shuiwang Ji · 2021
Cited alongside, same era.
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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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Regina Barzilay, and Tommi S Jaakkola · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Robust deep learning–based protein sequence design using ProteinMPNN
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, et al · 2022
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An autoregressive flow model for 3D molecular geometry generation from scratch
Youzhi Luo and Shuiwang Ji · 2022
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GraphEBM: Molecular graph generation with energy-based models
Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji · 2021
Cited alongside, same era.
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
Cited alongside, same era.
E(n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Lattice convolutional networks for learning ground states of quantum many-body systems
Cong Fu, Xuan Zhang, Huixin Zhang, Hongyi Ling, Shenglong Xu, and Shuiwang Ji · 2022
Cited alongside, same era.
Periodic graph transformers for crystal material property prediction
Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji · 2022
Cited alongside, same era.
Ig-VAE: Generative modeling of protein structure by direct 3d coordinate generation
Raphael R Eguchi, Christian A Choe, and Po-Ssu Huang · 2022
Cited alongside, same era.
Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Namrata Anand and Tudor Achim · 2022
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ProteinSGM: Score-based generative modeling for de novo protein design
Jin Sub Lee and Philip M Kim · 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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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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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 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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AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, et al · 2022
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Artificial intelligence for science in quantum, atomistic, and continuum systems, 2023
Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, YuQing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, and Shuiwang Ji · 2023
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Group equivariant fourier neural operators for partial differential equations
Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, and Shuiwang Ji · 2023
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Generating novel, designable, and diverse protein structures by equivariantly diffusing oriented residue clouds. 2023. doi: 10.48550
Lin Yeqing and AlQuraishi Mohammed · 2023
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Se (3) diffusion model with application to protein backbone generation
Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola · 2023
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