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Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions.
The use of position-specific rotamers in model building by homology
Glay Chinea, Gabriel Padrón, Rob W. W. Hooft, Chris Sander, and Gert Vriend · 1995
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A set of van der waals and coulombic radii of protein atoms for molecular and solvent-accessible surface calculation, packing evaluation, and docking
Ai-Jun Li and Ruth Nussinov · 1998
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Side-chain and backbone flexibility in protein core design
John R. Desjarlais and Tracy M. Handel · 1999
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Fast and accurate algorithms for protein side-chain packing
Jinbo Xu and Bonnie Berger · 2006
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Minimizing and learning energy functions for side-chain prediction
Chen Yanover, Ora Schueler-Furman, and Yair Weiss · 2007
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Protein contacts, inter-residue interactions and side-chain modelling
Guilhem Faure, Aurélie Bornot, and Alexandre G. de Brevern · 2008
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Improved prediction of protein side-chain conformations with scwrl4
Georgii G Krivov, Maxim V Shapovalov, and Roland L Dunbrack Jr · 2009
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Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Sidhartha Chaudhury, Sergey Lyskov, and Jeffrey J Gray · 2010
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A protein-dependent side-chain rotamer library
Md Shariful Islam Bhuyan and Xin Gao · 2011
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Improved side-chain modeling by coupling clash-detection guided iterative search with rotamer relaxation
Yang Cao, Lin Song, Zhichao Miao, Yun Hu, Liqing Tian, and Taijiao Jiang · 2011
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Fast and accurate prediction of protein side-chain conformations
Shide Liang, Dandan Zheng, Chi Zhang, and Daron M. Standley · 2011
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A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions
Maxim V Shapovalov and Roland L Dunbrack · 2011
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Sidepro: A novel machine learning approach for the fast and accurate prediction of side-chain conformations
Ken Nagata, Arlo Z. Randall, and Pierre Baldi · 2012
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Coupling protein side-chain and backbone flexibility improves the re-design of protein-ligand specificity
Noah Ollikainen, René M. de Jong, and Tanja Kortemme · 2015
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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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Protein secondary structure prediction using deep convolutional neural fields
Sheng Wang, Jian Peng, Jianzhu Ma, and Jinbo Xu · 2016
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The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F. Alford, Andrew Leaver-Fay, Jeliazko R. Jeliazkov, Matthew J. O’Meara, Frank DiMaio, Hahnbeom Park, Maxim V. Shapovalov, P. Douglas Renfrew, Vikram Khipple Mulligan, Kalli Kappel, Jason W. Labonte, Michael S. Pacella, Richard Bonneau, Philip Bradley, Roland L. Dunbrack, Rhiju Das, David Baker, Brian Kuhlman, Tanja Kortemme, and Jeffrey J. Gray · 2017
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3.13 computational methods related to molecular structure and reaction chemistry of biomaterials
Samaneh Farokhirad, Ryan P Bradley, Arijit Sarkar, Andrew J Shih, Shannon E. Telesco, Yingting Liu, Ravindra Venkatramani, David M. Eckmann, Portonovo S. Ayyaswamy, and Ravi Radhakrishnan · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Prediction of amino acid side chain conformation using a deep neural network
Ke Liu, Xiangyan Sun, Jun Ma, Zhenyu Zhou, Qilin Dong, Shengwen Peng, Junqiu Wu, Suocheng Tan, Günter Blobel, and Jie Fan · 2017
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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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Propose: Direct exhaustive protein-protein docking with side chain flexibility
Hervé Hogues, Francis Gaudreault, Christopher R. Corbeil, Christophe Deprez, Traian Sulea, and Enrico O. Purisima · 2018
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Neural message passing with edge updates for predicting properties of molecules and materials
Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, and Mikkel N Schmidt · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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A structural homology approach for computational protein design with flexible backbone
David Simoncini, Kam Y. J. Zhang, T. Schiex, and Sophie Barbe · 2018
Cited alongside, same era.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess E. Smidt, Steven M. Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick F. Riley · 2018
Cited alongside, same era.
Computational reconstruction of atomistic protein structures from coarse-grained models
Aleksandra E. Badaczewska-Dawid, Andrzej Kolinski, and Sebastian Kmiecik · 2019
Gemnet: Universal directional graph neural networks for molecules
Johannes Klicpera, Florian Becker, and Stephan Günnemann · 2021
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Spherical message passing for 3d graph networks
Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
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E(n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 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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Fast end-to-end learning on protein surfaces
Freyr Sverrisson, Jean Feydy, Bruno E Correia, and Michael M Bronstein · 2021
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Cited alongside, same era.
Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
Cited alongside, same era.
Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
Cited alongside, same era.
Graphqa: protein model quality assessment using graph convolutional networks
Federico Baldassarre, David Ménendez Hurtado, Arne Elofsson, and Hossein Azizpour · 2020
Cited alongside, same era.
Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2020
Cited alongside, same era.
Se(3)-transformers: 3d roto-translation equivariant attention networks
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Pablo Gainza, Freyr Sverrisson, Frederico Monti, Emanuele Rodola, D Boscaini, MM Bronstein, and BE Correia · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Opus-rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors
Gang Xu, Qinghua Wang, and Jianpeng Ma · 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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Contrastive representation learning for 3d protein structures
Pedro Hermosilla and Timo Ropinski · 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 S. Jaakkola · 2022
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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Shitong Luo, Yufeng Su, Xingang Peng, Sheng Wang, Jian Peng, and Jianzhu Ma · 2022
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Attnpacker: An end-to-end deep learning method for rotamer-free protein side-chain packing
Matthew McPartlon and Jinbo Xu · 2022
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Dlpacker: deep learning for prediction of amino acid side chain conformations in proteins
Mikita Misiura, Raghav Shroff, Ross Thyer, and Anatoly B Kolomeisky · 2022
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Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 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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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 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 · 2023
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Yeqing Lin and Mohammed AlQuraishi · 2023
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Molecular geometry pretraining with SE(3)-invariant denoising distance matching
Shengchao Liu, Hongyu Guo, and Jian Tang · 2023
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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 S. Jaakkola · 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, A. Doucet, Regina Barzilay, and T. Jaakkola · 2023
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