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Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences or structures, neglecting the exploration of their joint distribution, which is crucial for a comprehensive understanding of protein functions by integrating co-evolutionary information and structural characteristics.
The formation and stabilization of protein structure
Christian B Anfinsen · 1972
Earlier work this paper cites.
Asymptotic evaluation of certain markov process expectations for large time. iv
Monroe D Donsker and SR Srinivasa Varadhan · 1983
Earlier work this paper cites.
Exploring the conformational energy landscape of proteins
G Ulrich Nienhaus, Joachim D Müller, Ben H McMahon, and Hans Frauenfelder · 1997
Earlier work this paper cites.
Jensen-shannon divergence and hilbert space embedding
Bent Fuglede and Flemming Topsoe · 2004
Earlier work this paper cites.
Development and testing of a general amber force field
Junmei Wang, Romain M Wolf, James W Caldwell, Peter A Kollman, and David A Case · 2004
Earlier work this paper cites.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
Earlier work this paper cites.
Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2009
Earlier work this paper cites.
Probing the flexibility of large conformational changes in protein structures through local perturbations
Bosco K Ho and David A Agard · 2009
Earlier work this paper cites.
Large conformational changes in proteins: signaling and other functions
Barry J Grant, Alemayehu A Gorfe, and J Andrew McCammon · 2010
Earlier work this paper cites.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
Earlier work this paper cites.
Essentials of cell biology
Clare M O’Connor, Jill U Adams, and Jennifer Fairman · 2010
Earlier work this paper cites.
A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions
Maxim V Shapovalov and Roland L Dunbrack Jr · 2011
Earlier work this paper cites.
The protein-folding problem, 50 years on
Ken A Dill and Justin L MacCallum · 2012
Earlier work this paper cites.
Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Michael U Gutmann and Aapo Hyvärinen · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Adaptive noise schedule for denoising autoencoder
B Chandra and Rajesh Kumar Sharma · 2014
Earlier work this paper cites.
Scheduled denoising autoencoders
Krzysztof J Geras and Charles Sutton · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm · 2018
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 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.
Critical assessment of methods of protein structure prediction (casp)—round xiii
Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, and John Moult · 2019
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Evaluating protein transfer learning with tape
Roshan Rao, Nicholas Bhattacharya, Neil Thomas, Yan Duan, Peter Chen, John Canny, Pieter Abbeel, and Yun Song · 2019
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.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
Cited alongside, same era.
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 · 2021
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Self-supervised graph-level representation learning with local and global structure
Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang · 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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Structure-aware protein self-supervised learning
Can Chen, Jingbo Zhou, Fan Wang, Xue Liu, and Dejing Dou · 2022
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Self-supervised pre-training for protein embeddings using tertiary structures
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Equivariant flows: exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 2020
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Self-supervised contrastive learning of protein representations by mutual information maximization
Amy X Lu, Haoran Zhang, Marzyeh Ghassemi, and Alan M Moses · 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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Atom3d: Tasks on molecules in three dimensions
Raphael JL Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, et al · 2020
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Diffusion-based representation learning
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Structured denoising diffusion models in discrete state-spaces
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Contrastive representation learning for 3d protein structures
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Equivariant diffusion for molecule generation in 3d
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Torsional diffusion for molecular conformer generation
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Diffusion-lm improves controllable text generation
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Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
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Digress: Discrete denoising diffusion for graph generation
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Learning protein representations via complete 3d graph networks
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
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Masked inverse folding with sequence transfer for protein representation learning
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Cross-modality and self-supervised protein embedding for compound–protein affinity and contact prediction
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