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How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attention in recent years; however, few methods can simultaneously improve the accuracy and efficiency due to the lack of expressive features and autoregressive sequence decoder.
Molecular technology: designing proteins and peptides
Carl Pabo · 1983
Earlier work this paper cites.
Cath–a hierarchic classification of protein domain structures
Christine A Orengo, Alex D Michie, Susan Jones, David T Jones, Mark B Swindells, and Janet M Thornton · 1997
Earlier work this paper cites.
The limits of protein sequence comparison?
William R Pearson and Michael L Sierk · 2005
Earlier work this paper cites.
3d representations of amino acids—applications to protein sequence comparison and classification
Jie Li and Patrice Koehl · 2014
Earlier work this paper cites.
Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
Zhixiu Li, Yuedong Yang, Eshel Faraggi, Jian Zhan, and Yaoqi Zhou · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Spherical convolutions and their application in molecular modelling
Wouter Boomsma and Jes Frellsen · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
3d deep convolutional neural networks for amino acid environment similarity analysis
Wen Torng and Russ B Altman · 2017
Earlier work this paper cites.
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Earlier work this paper cites.
Design of metalloproteins and novel protein folds using variational autoencoders
Joe G Greener, Lewis Moffat, and David T Jones · 2018
Earlier work this paper cites.
Non-autoregressive neural machine translation
Jiatao Gu, James Bradbury, Caiming Xiong, Victor OK Li, and Richard Socher · 2018
Earlier work this paper cites.
Spin2: Predicting sequence profiles from protein structures using deep neural networks
James O’Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, and Yaoqi Zhou · 2018
Earlier work this paper cites.
Computational protein design with deep learning neural networks
Jingxue Wang, Huali Cao, John ZH Zhang, and Yifei Qi · 2018
Earlier work this paper cites.
3d steerable cnns: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco S Cohen · 2018
Earlier work this paper cites.
To improve protein sequence profile prediction through image captioning on pairwise residue distance map
Sheng Chen, Zhe Sun, Lihua Lin, Zifeng Liu, Xun Liu, Yutian Chong, Yutong Lu, Huiying Zhao, and Yuedong Yang · 2019
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Mask-predict: Parallel decoding of conditional masked language models
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer · 2019
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Generative models for graph-based protein design
John Ingraham, Vikas K Garg, Regina Barzilay, and Tommi Jaakkola · 2019
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Non-autoregressive machine translation with auxiliary regularization
Yiren Wang, Fei Tian, Di He, Tao Qin, ChengXiang Zhai, and Tie-Yan Liu · 2019
Cited alongside, same era.
Deep learning in protein structural modeling and design
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J Gray · 2020
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Momentum contrast for unsupervised visual representation learning
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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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Protein sequence design with a learned potential
Namrata Anand, Raphael Eguchi, Irimpan I Mathews, Carla P Perez, Alexander Derry, Russ B Altman, and Po-Ssu Huang · 2022
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A survey on generative diffusion model
Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li · 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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