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Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design.
Lga: a method for finding 3d similarities in protein structures
Zemla, A · 2003
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The protein-folding problem, 50 years on
Dill, K. A. and MacCallum, J. L · 2012
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lddt: a local superposition-free score for comparing protein structures and models using distance difference tests
Mariani, V., Biasini, M., Barbato, A., and Schwede, T · 2013
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Enzynet: enzyme classification using 3d convolutional neural networks on spatial representation
Amidi, A., Amidi, S., Vlachakis, D., Megalooikonomou, V., Paragios, N., and Zacharaki, E. I · 2018
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Deep convolutional networks for quality assessment of protein folds
Derevyanko, G., Grudinin, S., Bengio, Y., and Lamoureux, G · 2018
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Methods for the refinement of protein structure 3d models
Adiyaman, R. and McGuffin, L. J · 2019
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Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
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Evaluating protein transfer learning with tape
Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, P., Canny, J., Abbeel, P., and Song, Y · 2019
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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., and Fergus, R · 2019
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Learning optimal representations with the decodable information bottleneck
Dubois, Y., Kiela, D., Schwab, D. J., and Vedantam, R · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J., and Dror, R · 2020
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Transformer protein language models are unsupervised structure learners
Rao, R. M., Meier, J., Sercu, T., Ovchinnikov, S., and Rives, A · 2020
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Graphqa: protein model quality assessment using graph convolutional networks
Baldassarre, F., Menéndez Hurtado, D., Elofsson, A., and Azizpour, H · 2021
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Graphms: Drug target prediction using graph representation learning with substructures
Cheng, S., Zhang, L., Jin, B., Zhang, Q., Lu, X., You, M., and Tian, X · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Improved protein structure refinement guided by deep learning based accuracy estimation
Hiranuma, N., Park, H., Baek, M., Anishchenko, I., Dauparas, J., and Baker, D · 2021
Cited alongside, same era.
Fast and effective protein model refinement using deep graph neural networks
Jing, X. and Xu, J · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Contrastive representation learning for 3d protein structures
Hermosilla, P. and Ropinski, T · 2022
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Learning inverse folding from millions of predicted structures
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., and Rives, A · 2022
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Illuminating protein space with a programmable generative model
Ingraham, J., Baranov, M., Costello, Z., Frappier, V., Ismail, A., Tie, S., Wang, W., Xue, V., Obermeyer, F., Beam, A., et al · 2022
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Denoising diffusion probabilistic models on so (3) for rotational alignment
Leach, A., Schmon, S. M., Degiacomi, M. T., and Willcocks, C. G · 2022
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Proteinsgm: Score-based generative modeling for de novo protein design
Lee, J. S. and Kim, P. M · 2022
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Diffusion probabilistic models for 3d point cloud generation
Luo, S. and Hu, W · 2021
Cited alongside, same era.
Toward more general embeddings for protein design: Harnessing joint representations of sequence and structure
Mansoor, S., Baek, M., Madan, U., and Horvitz, E · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Msa transformer
Rao, R. M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., and Rives, A · 2021
Cited alongside, same era.
Deciphering antibody affinity maturation with language models and weakly supervised learning
Ruffolo, J. A., Gray, J. J., and Sulam, J · 2021
Cited alongside, same era.
Evaluation of model refinement in casp14
Simpkin, A. J., Sanchez Rodriguez, F., Mesdaghi, S., Kryshtafovych, A., and Rigden, D. J · 2021
Cited alongside, same era.
Lm-gvp: A generalizable deep learning framework for protein property prediction from sequence and structure
Wang, Z., Combs, S. A., Brand, R., Calvo, M. R., Xu, P., Price, G., Golovach, N., Salawu, E. O., Wise, C. J., Ponnapalli, S. P., et al · 2021
Cited alongside, same era.
Antigen-specific antibody design and optimization with diffusion-based generative models
Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., and Ma, J · 2022
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State-of-the-art estimation of protein model accuracy using alphafold
Roney, J. P. and Ovchinnikov, S · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Trippe, B. L., Yim, J., Tischer, D., Broderick, T., Baker, D., Barzilay, R., and Jaakkola, T · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2022
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Atomic protein structure refinement using all-atom graph representations and se (3)-equivariant graph neural networks
Wu, T. and Cheng, J · 2022
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Convolutions are competitive with transformers for protein sequence pretraining
Yang, K. K., Lu, A. X., and Fusi, N. K · 2022
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Cross-modality and self-supervised protein embedding for compound–protein affinity and contact prediction
You, Y. and Shen, Y · 2022
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Protein representation learning by geometric structure pretraining
Zhang, Z., Xu, M., Jamasb, A., Chenthamarakshan, V., Lozano, A., Das, P., and Tang, J · 2022
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2022
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