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Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics.
SCOP: a structural classification of proteins database for the investigation of sequences and structures
A.G. Murzin, S.E. Brenner, T. Hubbard, and C. Chothia · 1955
Earlier work this paper cites.
Self-organizing neural network that discovers surfaces in random-dot stereograms
Suzanna Becker and Geoffrey E. Hinton · 1992
Earlier work this paper cites.
The Protein Data Bank
Helen M. Berman, John Westbrook, Zukang Feng, Gary Gilliland, T. N. Bhat, Helge Weissig, Ilya N. Shindyalov, and Philip E. Bourne · 2000
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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The natural history of protein domains
Chris P. Ponting and Robert R. Russell · 2002
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Automatic classification of protein structure by using gauss integrals
Peter Røgen and Boris Fain · 2003
Earlier work this paper cites.
Protein function prediction via graph kernels
Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alex J. Smola, and Hans-Peter Kriegel · 2005
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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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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Secondary structure spatial conformation footprint: a novel method for fast protein structure comparison and classification
Elena Zotenko, Dianne P. O’Leary, and Teresa M. Przytycka · 2006
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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3D-SURFER: software for high-throughput protein surface comparison and analysis
David La, Juan Esquivel-Rodríguez, Vishwesh Venkatraman, Bin Li, Lee Sael, Stephen Ueng, Steven Ahrendt, and Daisuke Kihara · 2009
Earlier work this paper cites.
Protein structure alignment beyond spatial proximity
Sheng Wang, Jianzhu Ma, Jian Peng, and Jinbo Xu · 2013
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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Continuous distributed representation of biological sequences for deep proteomics and genomics
E. Asgari and M.R.K. Mofrad · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
A. Amidi, S. Amidi, D. Vlachakis, V. Megalooikonomou, N. Paragios, and E. Zacharaki · 2017
Earlier work this paper cites.
Protein interface prediction using graph convolutional networks
A. Fout, J. Byrd, B. Shariat, and A. Ben-Hur · 2017
Earlier work this paper cites.
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
J Jiménez, S Doerr, G Martínez-Rosell, and A S Rose · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Earlier work this paper cites.
Forging the basis for developing protein–ligand interaction scoring functions
Zhihai Liu, Minyi Su, Li Han, Jie Liu, Qifan Yang, Yan Li, and Renxiao Wang · 2017
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Protein–ligand scoring with convolutional neural networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
SIFTS: updated Structure Integration with Function, Taxonomy and Sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
J. M Dana, A. Gutmanas, N. Tyagi, G. Qi, C. O’Donovan, M. Martin, and S. Velankar · 2018
Cited alongside, same era.
Deep convolutional networks for quality assessment of protein folds
Georgy Derevyanko, Sergei Grudinin, Yoshua Bengio, and Guillaume Lamoureux · 2018
Cited alongside, same era.
mtm-align: a server for fast protein structure database search and multiple protein structure alignment
Runze Dong, Shuo Pan, Zhenling Peng, Yang Zhang, and Jianyi Yang · 2018
Cited alongside, same era.
Deepsf: Deep convolutional neural network for mapping protein sequences to folds
J. Hou, B. Adhikari, and J. Cheng · 2018
Cited alongside, same era.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
P. Gainza, F. Sverrisson, F. Monti, E. Rodolà, D. Boscaini, M. M. Bronstein, and B. E. Correia · 2020
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Real time structural search of the protein data bank
Dmytro Guzenko, Stephen K. Burley, and Jose M. Duarte · 2020
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Zinc20—a free ultralarge-scale chemical database for ligand discovery
John J. Irwin, Khanh G. Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R. Wong, Munkhzul Khurelbaatar, Yurii S. Moroz, John Mayfield, and Roger A. Sayle · 2020
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
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Multi-domain protein shape retrieval challenge
Florent Langenfeld, Yuxu Peng, Yu Kun Lai, Paul L. Rosin, Tunde Aderinwale, Genki Terashi, Charles Christoffer, Daisuke Kihara, Halim Benhabiles, Karim Hammoudi, Adnane Cabani, Feryal Windal, Mahmoud Melkemi, Andrea Giachetti, Stelios Mylonas, Apostolos Axenopoulos, Petros Daras, Ekpo Otu, Reyer Zwiggelaar, and David Hunter · 2020
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Learning structural motif representations for efficient protein structure search
Yang Liu, Qing Ye, Liwei Wang, and Jian Peng · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Cited alongside, same era.
DeepDTA: deep drug–target binding affinity prediction
Hakime Öztürk, Arzucan Özgür, and Elif Ozkirimli · 2018
Cited alongside, same era.
Unified rational protein engineering with sequence-based deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M. Church · 2019
Cited alongside, same era.
Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Pre-training of deep bidirectional protein sequence representations with structural information
Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, and Sungroh Yoon · 2020
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Pfam: The protein families database in 2021
Jaina Mistry, Sara Chuguransky, Lowri Williams, Matloob Qureshi, Gustavo A Salazar, Erik L L Sonnhammer, Silvio C E Tosatto, Lisanna Paladin, Shriya Raj, Lorna J Richardson, Robert D Finn, and Alex Bateman · 2020
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Is transfer learning necessary for protein landscape prediction?, 2020
Amir Shanehsazzadeh, David Belanger, and David Dohan · 2020
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Udsmprot: universal deep sequence models for protein classification
Nils Strodthoff, Patrick Wagner, Markus Wenzel, and Wojciech Samek · 2020
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Fast and flexible protein design using deep graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M. Kim · 2020
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ATOM3D: tasks on molecules in three dimensions
Raphael J. L. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Brandon M. Anderson, Stephan Eismann, Risi Kondor, Russ B. Altman, and Ron O. Dror · 2020
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On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, and Mario Lucic · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Vector neurons: a general framework for so(3)-equivariant networks
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas Guibas · 2021
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Structure-based function prediction using graph convolutional networks
V. Gligorijevic, P. D. Renfrew, T. Kosciolek, J. K. Leman, K. Cho, T. Vatanen, D. Berenberg, B. Taylor, I. M. Fisk, R. J. Xavier, R. Knight, and R. Bonneau · 2021
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Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures
Pedro Hermosilla, Marco Schäfer, Matěj Lang, Gloria Fackelmann, Pere Pau Vázquez, Barbora Kozlíková, Michael Krone, Tobias Ritschel, and Timo Ropinski · 2021
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2021
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2021
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Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 2021
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Lm-gvp: an extensible sequence and structure informed deep learning framework for protein property prediction
Zichen Wang, Steven A. Combs, Ryan Brand, Miguel Romero Calvo, Panpan Xu, George Price, Nataliya Golovach, Emmanuel O. Salawu, Colby J. Wise, Sri Priya Ponnapalli, and Peter M. Clark · 2022
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Pre-training of deep protein models with molecular dynamics simulations for drug binding, 2022
Fang Wu, Qiang Zhang, Dragomir Radev, Yuyang Wang, Xurui Jin, Yinghui Jiang, Zhangming Niu, and Stan Z. Li · 2022
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Fast protein structure comparison through effective representation learning with contrastive graph neural networks
Chunqiu Xia, Shi-Hao Feng, Ying Xia, Xiaoyong Pan, and Hong-Bin Shen · 2022
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Protein representation learning by geometric structure pretraining, 2022
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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