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Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions.
Enzyme nomenclature
Webb, O. F.; Phelps, T. J.; Bienkowski, P. R.; Digrazia, P. M.; White, D. C.; and Sayler, G. S. 1992 · 1992
Earlier work this paper cites.
The protein data bank
Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; and Bourne, P. E. 2000 · 2000
Earlier work this paper cites.
The natural history of protein domains
Ponting, C. P.; and Russell, R. R. 2002 · 2002
Earlier work this paper cites.
Is transfer learning necessary for protein landscape prediction?
Shanehsazzadeh, A.; Belanger, D.; and Dohan, D. 2020 · 2011
Earlier work this paper cites.
A large-scale evaluation of computational protein function prediction
Radivojac, P.; Clark, W. T.; Oron, T. R.; Schnoes, A. M.; Wittkop, T.; Sokolov, A.; Graim, K.; Funk, C.; Verspoor, K.; Ben-Hur, A.; et al. 2013 · 2013
Earlier work this paper cites.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
Unified rational protein engineering with sequence-based deep representation learning
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Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Earlier work this paper cites.
Critical assessment of methods of protein structure prediction (CASP)—Round XIII
Kryshtafovych, A.; Schwede, T.; Topf, M.; Fidelis, K.; and Moult, J. 2019 · 2019
Earlier work this paper cites.
Evaluating Protein Transfer Learning with TAPE
Rao, R.; Bhattacharya, N.; Thomas, N.; Duan, Y.; Chen, X.; Canny, J.; Abbeel, P.; and Song, Y. S. 2019 · 2019
Earlier work this paper cites.
In Advances in Neural Information Processing Systems
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2020
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. 2020 · 2020
Earlier work this paper cites.
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gainza, P.; Sverrisson, F.; Monti, F.; Rodola, E.; Boscaini, D.; Bronstein, M.; and Correia, B. 2020 · 2020
Earlier work this paper cites.
Self-supervised contrastive learning of protein representations by mutual information maximization
Lu, A. X.; Zhang, H.; Ghassemi, M.; and Moses, A. M. 2020 · 2020
Earlier work this paper cites.
Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M.; DiMaio, F.; Anishchenko, I.; Dauparas, J.; Ovchinnikov, S.; Lee, G. R.; Wang, J.; Cong, Q.; Kinch, L. N.; Schaeffer, R. D.; et al. 2021 · 2021
Earlier work this paper cites.
Structure-based protein function prediction using graph convolutional networks
Gligorijević, V.; Renfrew, P. D.; Kosciolek, T.; Leman, J. K.; Berenberg, D.; Vatanen, T.; Chandler, C.; Taylor, B. C.; Fisk, I. M.; Vlamakis, H.; et al. 2021 · 2021
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Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
He, L.; Zhang, S.; Wu, L.; Xia, H.; Ju, F.; Zhang, H.; Liu, S.; Xia, Y.; Zhu, J.; Deng, P.; et al. 2021 · 2021
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Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
Hermosilla, P.; Schäfer, M.; Lang, M.; Fackelmann, G.; Vázquez, P. P.; Kozlíková, B.; Krone, M.; Ritschel, T.; and Ropinski, T. 2021 · 2021
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Learning from Protein Structure with Geometric Vector Perceptrons
Jing, B.; Eismann, S.; Soni, P. N.; and Dror, R. O. 2021 · 2021
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Highly accurate protein structure prediction with AlphaFold
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; et al. 2021 · 2021
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Notin, P.; Dias, M.; Frazer, J.; Hurtado, J. M.; Gomez, A. N.; Marks, D.; and Gal, Y. 2022 · 2022
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PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
Xu, M.; Zhang, Z.; Lu, J.; Zhu, Z.; Zhang, Y.; Ma, C.; Liu, R.; and Tang, J. 2022 · 2022
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Protein Representation Learning by Geometric Structure Pretraining
Zhang, Z.; Xu, M.; Jamasb, A. R.; Chenthamarakshan, V.; Lozano, A.; Das, P.; and Tang, J. 2022b · 2022
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TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
Zhu, Z.; Shi, C.; Zhang, Z.; Liu, S.; Xu, M.; Yuan, X.; Zhang, Y.; Chen, J.; Cai, H.; Lu, J.; et al. 2022 · 2022
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xtrimopglm: Unified 100b-scale pre-trained transformer for deciphering the language of protein
Chen, B.; Cheng, X.; Geng, Y.-a.; Li, S.; Zeng, X.; Wang, B.; Gong, J.; Liu, C.; Zeng, A.; Dong, Y.; et al. 2023a · 2023
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Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J.; Rao, R.; Verkuil, R.; Liu, J.; Sercu, T.; and Rives, A. 2021 · 2021
Cited alongside, same era.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A.; Meier, J.; Sercu, T.; Goyal, S.; Lin, Z.; Liu, J.; Guo, D.; Ott, M.; Zitnick, C. L.; Ma, J.; et al. 2021 · 2021
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Fast end-to-end learning on protein surfaces
Sverrisson, F.; Feydy, J.; Correia, B. E.; and Bronstein, M. M. 2021 · 2021
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ATOM3D: Tasks on Molecules in Three Dimensions
Townshend, R. J. L.; Vögele, M.; Suriana, P. A.; Derry, A.; Powers, A.; Laloudakis, Y.; Balachandar, S.; Jing, B.; Anderson, B. M.; Eismann, S.; Kondor, R.; Altman, R.; and Dror, R. O. 2021 · 2021
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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
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Robust deep learning–based protein sequence design using ProteinMPNN
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Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures
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Evolutionary-scale prediction of atomic-level protein structure with a language model
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Large language models generate functional protein sequences across diverse families
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Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem
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EurNet: Efficient Multi-Range Relational Modeling of Protein Structure
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