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Computational antibody design seeks to automatically create an antibody that binds to an antigen.
A solution for the best rotation to relate two sets of vectors
Kabsch, W · 1976
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Stable calculation of coordinates from distance information
Crippen, G. M. and Havel, T. F · 1978
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SAbDab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., and Deane, C. M · 2014
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AbDesign: A n algorithm for combinatorial backbone design guided by natural conformations and sequences
Lapidoth, G. D., Baran, D., Pszolla, G. M., Norn, C., Alon, A., Tyka, M. D., and Fleishman, S. J · 2015
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Dockq: a quality measure for protein-protein docking models
Basu, S. and Wallner, B · 2016
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Openmm 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al · 2017
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Protein interface prediction using graph convolutional networks
Fout, A., Byrd, J., Shariat, B., and Ben-Hur, A · 2017
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The cluspro web server for protein–protein docking
Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., and Vajda, S · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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RosettaAntibodyDesign (RAbD): A general framework for computational antibody design
Adolf-Bryfogle, J., Kalyuzhniy, O., Kubitz, M., Weitzner, B. D., Hu, X., Adachi, Y., Schief, W. R., and Dunbrack Jr, R. L · 2018
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Learning protein structure with a differentiable simulator
Ingraham, J., Riesselman, A., Sander, C., and Marks, D · 2018
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Foldx 5.0: working with rna, small molecules and a new graphical interface
Delgado, J., Radusky, L. G., Cianferoni, D., and Serrano, L · 2019
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Generative models for graph-based protein design
Ingraham, J., Garg, V. K., Barzilay, R., and Jaakkola, T · 2019
Cited alongside, same era.
End-to-end learning on 3d protein structure for interface prediction
Townshend, R., Bedi, R., Suriana, P., and Dror, R · 2019
Cited alongside, same era.
De novo protein design for novel folds using guided conditional wasserstein generative adversarial networks
Karimi, M., Zhu, S., Cao, Y., and Shen, Y · 2020
Cited alongside, same era.
Antibody complementarity determining region design using high-capacity machine learning
Liu, G., Zeng, H., Mueller, J., Carter, B., Wang, Z., Schilz, J., Horny, G., Birnbaum, M. E., Ewert, S., and Gifford, D. K · 2020
Cited alongside, same era.
Fast and flexible design of novel proteins using graph neural networks
Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., and Kim, P. M · 2020
Cited alongside, same era.
Protein complex prediction with alphafold-multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A. W., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., et al · 2021
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Independent SE(3)-equivariant models for end-to-end rigid protein docking
Ganea, O.-E., Huang, X., Bunne, C., Bian, Y., Barzilay, R., Jaakkola, T., and Krause, A · 2021
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Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, W., Wohlwend, J., Barzilay, R., and Jaakkola, T · 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
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When attention meets fast recurrence: Training language models with reduced compute
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Tischer, D., Lisanza, S., Wang, J., Dong, R., Anishchenko, I., Milles, L. F., Ovchinnikov, S., and Baker, D · 2020
Cited alongside, same era.
The hdock server for integrated protein–protein docking
Yan, Y., Tao, H., He, J., and Huang, S.-Y · 2020
Cited alongside, same era.
A highly conserved cryptic epitope in the receptor binding domains of sars-cov-2 and sars-cov
Yuan, M., Wu, N. C., Zhu, X., Lee, C.-C. D., So, R. T., Lv, H., Mok, C. K., and Wilson, I. A · 2020
Cited alongside, same era.
In silico proof of principle of machine learning-based antibody design at unconstrained scale
Akbar, R., Robert, P. A., Weber, C. R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A., et al · 2021
Cited alongside, same era.
Neural message passing for joint paratope-epitope prediction
Del Vecchio, A., Deac, A., Liò, P., and Veličković, P · 2021
Cited alongside, same era.
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Eismann, S., Townshend, R. J., Thomas, N., Jagota, M., Jing, B., and Dror, R. O · 2021
Cited alongside, same era.
Robust de novo design of protein binding proteins from target structural information alone
Cao, L., Coventry, B., Goreshnik, I., Huang, B., Park, J. S., Jude, K. M., Marković, I., Kadam, R. U., Verschueren, K. H., Verstraete, K., et al
Cited in the paper.
Lei, T · 2021
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Protein sequence design by conformational landscape optimization
Norn, C., Wicky, B. I., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Baker, D., and Ovchinnikov, S · 2021
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Antibody design using lstm based deep generative model from phage display library for affinity maturation
Saka, K., Kakuzaki, T., Metsugi, S., Kashiwagi, D., Yoshida, K., Wada, M., Tsunoda, H., and Teramoto, R · 2021
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E(n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
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Protein design and variant prediction using autoregressive generative models
Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., and Marks, D. S · 2021
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Multi-scale representation learning on proteins
Somnath, V. R., Bunne, C., and Krause, A · 2021
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Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Ruffolo, J. A. and Gray, J. J · 2022
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