Fetching the paper…
Reading the bibliography…
Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system.
A solution for the best rotation to relate two sets of vectors
Kabsch, W · 1976
Earlier work this paper cites.
A simple method for displaying the hydropathic character of a protein
Kyte, J. and Doolittle, R. F · 1982
Earlier work this paper cites.
Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence
Guruprasad, K., Reddy, B. B., and Pandit, M. W · 1990
Earlier work this paper cites.
Hydrophobicity, expressivity and aromaticity are the major trends of amino-acid usage in 999 escherichia coli chromosome-encoded genes
Lobry, J. and Gautier, C · 1994
Earlier work this paper cites.
Imgt unique numbering for immunoglobulin and t cell receptor variable domains and ig superfamily v-like domains
Lefranc, M.-P., Pommié, C., Ruiz, M., Giudicelli, V., Foulquier, E., Truong, L., Thouvenin-Contet, V., and Lefranc, G · 2003
Earlier work this paper cites.
Optcdr: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding
Pantazes, R. and Maranas, C. D · 2010
Earlier work this paper cites.
Computer-aided antibody design
Kuroda, D., Shirai, H., Jacobson, M. P., and Nakamura, H · 2012
Earlier work this paper cites.
Sabdab: the structural antibody database
Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., and Deane, C. M · 2014
Earlier work this paper cites.
Optmaven–a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes
Li, T., Pantazes, R. J., and Maranas, C. D · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Steinegger, M. and Söding, J · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Generative modeling for protein structures
Anand, N. and Huang, P · 2018
Earlier work this paper cites.
Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
Earlier work this paper cites.
Computational design of antibodies
Fischman, S. and Ofran, Y · 2018
Earlier work this paper cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2018
Earlier work this paper cites.
Spin2: Predicting sequence profiles from protein structures using deep neural networks
O’Connell, J., Li, Z., Hanson, J., Heffernan, R., Lyons, J., Paliwal, K., Dehzangi, A., Yang, Y., and Zhou, Y · 2018
Earlier work this paper cites.
Graphrnn: Generating realistic graphs with deep auto-regressive models
You, J., Ying, R., Ren, X., Hamilton, W., and Leskovec, J · 2018
Cited alongside, same era.
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., and Church, G. M · 2019
Cited alongside, same era.
Discrete and continuous deep residual learning over graphs
Avelar, P. H., Tavares, A. R., Gori, M., and Lamb, L. C · 2019
Cited alongside, same era.
Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
Cited alongside, same era.
Graph matching networks for learning the similarity of graph structured objects
Li, Y., Gu, C., Dullien, T., Vinyals, O., and Kohli, P · 2019
Cited alongside, same era.
Grand: Graph neural diffusion
Chamberlain, B., Rowbottom, J., Gorinova, M. I., Bronstein, M., Webb, S., and Rossi, E · 2021
Later among the works it cites.
Pde-gcn: Novel architectures for graph neural networks motivated by partial differential equations
Eliasof, M., Haber, E., and Treister, E · 2021
Later among the works it cites.
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
Later among the works it cites.
Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., et al · 2021
Later among the works it cites.
E(n) equivariant graph neural networks, 2021
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Cited alongside, same era.
Five computational developability guidelines for therapeutic antibody profiling
Raybould, M. I., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A., Shi, J., and Deane, C. M · 2019
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 · 2019
Cited alongside, same era.
A review of deep learning methods for antibodies
Graves, J., Byerly, J., Priego, E., Makkapati, N., Parish, S. V., Medellin, B., and Berrondo, M · 2020
Cited alongside, same era.
Learning continuous-time pdes from sparse data with graph neural networks
Iakovlev, V., Heinonen, M., and Lähdesmäki, H · 2020
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J., and Dror, R · 2020
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.
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 · 2022
Later among the works it cites.
Robust deep learning–based protein sequence design using proteinmpnn
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I., Courbet, A., de Haas, R. J., Bethel, N., et al · 2022
Later among the works it cites.
Alphadesign: A graph protein design method and benchmark on alphafolddb, 2022
Gao, Z., Tan, C., and Li, S. Z · 2022
Later among the works it cites.
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
Later among the works it cites.
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., and Grigoryan, G · 2022
Later among the works it cites.
Equibind: Geometric deep learning for drug binding structure prediction
Stärk, H., Ganea, O., Pattanaik, L., Barzilay, R., and Jaakkola, T · 2022
Later among the works it cites.
Modular flows: Differential molecular generation
Verma, Y., Kaski, S., Heinonen, M., and Garg, V · 2022
Later among the works it cites.
Protein structure generation via folding diffusion, 2022
Wu, K. E., Yang, K. K., van den Berg, R., Zou, J. Y., Lu, A. X., and Amini, A. P · 2022
Later among the works it cites.
Diffdock: Diffusion steps, twists, and turns for molecular docking, 2023
Corso, G., Stärk, H., Jing, B., Barzilay, R., and Jaakkola, T · 2023
Closest in time.
Conditional antibody design as 3d equivariant graph translation, 2023
Kong, X., Huang, W., and Liu, Y · 2023
Closest in time.
Protein sequence and structure co-design with equivariant translation, 2023
Shi, C., Wang, C., Lu, J., Zhong, B., and Tang, J · 2023
Closest in time.