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Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure.
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Fold2seq: A joint sequence(1d)-fold(3d) embedding-based generative model for protein design
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Prottrans: Towards cracking the language of lifes code through self-supervised deep learning and high performance computing
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Clustering huge protein sequence sets in linear time
Martin Steinegger and Johannes Söding · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church · 2019
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Learning protein sequence embeddings using information from structure
Tristan Bepler and Bonnie Berger · 2019
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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
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Benchmarking fold detection by dalilite v.5
Liisa Holm · 2019
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Strategies for pre-training graph neural networks
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Pre-training co-evolutionary protein representation via a pairwise masked language model
Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, et al · 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, Pratham N. Soni, and Ron O. Dror · 2021
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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
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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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Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alexander Rives · 2021
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Pfam: The protein families database in 2021
Jaina Mistry, Sara Chuguransky, Lowri Williams, Matloob Qureshi, Gustavo A Salazar, Erik LL Sonnhammer, Silvio CE Tosatto, Lisanna Paladin, Shriya Raj, Lorna J Richardson, et al · 2021
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Msa transformer
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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, et al · 2021
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E (n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 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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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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Gbpnet: Universal geometric representation learning on protein structures
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