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Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition.
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Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design
Clark, L.A., Boriack-Sjodin, P.A., Eldredge, J., Fitch, C., Friedman, B., Hanf, K.J., Jarpe, M., Liparoto, S.F., Li, Y., Lugovskoy, A., et al., 2006 · 2006
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Computational design of antibody-affinity improvement beyond in vivo maturation
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An antibody loop replacement design feasibility study and a loop-swapped dimer structure
Clark, L.A., Boriack-Sjodin, P.A., Day, E., Eldredge, J., Fitch, C., Jarpe, M., Miller, S., Li, Y., Simon, K., Van Vlijmen, H.W., 2009 · 2009
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CPSP-web-tools: a server for 3D lattice protein studies
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Development trends for human monoclonal antibody therapeutics
Nelson, A.L., Dhimolea, E., Reichert, J.M., 2010 · 2010
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Snugdock: Paratope structural optimization during antibody-antigen docking compensates for errors in antibody homology models
Sircar, A., Gray, J.J., 2010 · 2010
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Restart strategies in optimization: parallel and serial cases
Shylo, O.V., Middelkoop, T., Pardalos, P.M., 2011 · 2011
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Sutskever, I., Martens, J., Hinton, G.E., 2011 · 2011
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Introduction to protein structure
Branden, C.I., Tooze, J., 2012 · 2012
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Application of asymmetric statistical potentials to antibody–protein docking
Brenke, R., Hall, D.R., Chuang, G.Y., Comeau, S.R., Bohnuud, T., Beglov, D., Schueler-Furman, O., Vajda, S., Kozakov, D., 2012 · 2012
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An empirical study of assumptions in bayesian optimisation
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Principles for designing ideal protein structures
Koga, N., Tatsumi-Koga, R., Liu, G., Xiao, R., Acton, T.B., Montelione, G.T., Baker, D., 2012 · 2012
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Paratome: An online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure
Kunik, V., Ashkenazi, S., Ofran, Y., 2012 · 2012
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Producing high-accuracy lattice models from protein atomic co-ordinates including side chains
Mann, M., Saunders, R., Smith, C., Backofen, R., Deane, C.M., 2012 · 2012
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Practical bayesian optimization of machine learning algorithms
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Computational and theoretical methods for protein folding
Compiani, M., Capriotti, E., 2013 · 2013
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Auto-encoding variational bayes
Kingma, D.P., Welling, M., 2013 · 2013
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Designing feature-controlled humanoid antibody discovery libraries using generative adversarial networks
Amimeur, T., Shaver, J.M., Ketchem, R.R., Taylor, J.A., Clark, R.H., Smith, J., Van Citters, D., Siska, C.C., Smidt, P., Sprague, M., et al., 2020 · 2020
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Predicting antibody developability profiles through early stage discovery screening, in: MAbs, Taylor & Francis. p. 1743053
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Kernels over sets of finite sets using rkhs embeddings, with application to bayesian (combinatorial) optimization, in: International Conference on Artificial Intelligence and Statistics, PMLR. pp. 2731–2741
Buathong, P., Ginsbourger, D., Krityakierne, T., 2020 · 2020
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Ig-vae: Generative modeling of immunoglobulin proteins by direct 3d coordinate generation
Eguchi, R.R., Anand, N., Choe, C.A., Huang, P.S., 2020 · 2020
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Antibody i-patch prediction of the antibody binding site improves rigid local antibody–antigen docking
Krawczyk, K., Baker, T., Shi, J., Deane, C.M., 2013 · 2013
Cited alongside, same era.
The structural basis of antibody-antigen recognition
Sela-Culang, I., Kunik, V., Ofran, Y., 2013 · 2013
Cited alongside, same era.
Second antibody modeling assessment (ama-ii)
Almagro, J.C., Teplyakov, A., Luo, J., Sweet, R.W., Kodangattil, S., Hernandez-Guzman, F., Gilliland, G.L., 2014 · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014 · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
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Improving b-cell epitope prediction and its application to global antibody-antigen docking
Krawczyk, K., Liu, X., Baker, T., Shi, J., Deane, C.M., 2014 · 2014
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Antibody specific epitope prediction—emergence of a new paradigm
Sela-Culang, I., Ofran, Y., Peters, B., 2015 · 2015
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Kurumida, Y., Saito, Y., Kameda, T., 2020 · 2020
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Boss: Bayesian optimization over string spaces
Moss, H., Leslie, D., Beck, D., Gonzalez, J., Rayson, P., 2020 · 2020
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mcsm-ab2: Guiding rational antibody design using graph-based signatures
Myung, Y., Rodrigues, C.H., Ascher, D.B., Pires, D.E., 2020 · 2020
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Computational approaches to therapeutic antibody design: Established methods and emerging trends
Norman, R.A., Ambrosetti, F., Bonvin, A.M., Colwell, L.J., Kelm, S., Kumar, S., Krawczyk, K., 2020 · 2020
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Understanding the human antibody repertoire, in: MAbs, Taylor & Francis. p. 1729683
Rees, A.R., 2020 · 2020
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Amortized bayesian optimization over discrete spaces, in: Conference on Uncertainty in Artificial Intelligence, PMLR. pp. 769–778
Swersky, K., Rubanova, Y., Dohan, D., Murphy, K., 2020 · 2020
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Turner, R., Eriksson, D., McCourt, M., Kiili, J., Laaksonen, E., Xu, Z., Guyon, I., 2021 · 2020
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Top companies and drugs by sales in 2020
Urquhart, L., 2021 · 2020
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A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation
Wang, M., Cang, Z., Wei, G.W., 2020 · 2020
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Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies
Akbar, R., Bashour, H., Rawat, P., Robert, P.A., Smorodina, E., Cotet, T.S., Karine, F.K., Frank, R., Mehta, B.B., Vu, M.H., Zengin, T., Gutierrez-Marcos, J., Lund-Johansen, F., Andersen, J.T., Greiff, V., 2022b · 2021
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Proteinbert: A universal deep-learning model of protein sequence and function
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., Linial, M., 2021 · 2021
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Fold2seq: A joint sequence (1d)-fold (3d) embedding-based generative model for protein design, in: International Conference on Machine Learning, PMLR. pp. 1261–1271
Cao, Y., Das, P., Chenthamarakshan, V., Chen, P.Y., Melnyk, I., Shen, Y., 2021 · 2021
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Neural message passing for joint paratope-epitope prediction
Del Vecchio, A., Deac, A., Liò, P., Veličković, P., 2021 · 2021
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Protein complex prediction with alphafold-multimer
Evans, R., O’Neill, M., Pritzel, A., Antropova, N., Senior, A., Green, T., Žídek, A., Bates, R., Blackwell, S., Yim, J., Ronneberger, O., Bodenstein, S., Zielinski, M., Bridgland, A., Potapenko, A., Cowie, A., Tunyasuvunakool, K., Jain, R., Clancy, E., Kohli, P., Jumper, J., Hassabis, D., 2021 · 2021
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An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants
Guest, J.D., Vreven, T., Zhou, J., Moal, I., Jeliazkov, J.R., Gray, J.J., Weng, Z., Pierce, B.G., 2021 · 2021
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Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, W., Wohlwend, J., Barzilay, R., Jaakkola, T., 2021 · 2021
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A review on genetic algorithm: Past, present, and future
Katoch, S., Chauhan, S., Kumar, V., 2021 · 2021
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Animal immunization, in vitro display technologies, and machine learning for antibody discovery
Laustsen, A.H., Greiff, V., Karatt-Vellatt, A., Muyldermans, S., Jenkins, T.P., 2021 · 2021
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Deciphering the language of antibodies using self-supervised learning
Leem, J., Mitchell, L.S., Farmery, J.H., Barton, J., Galson, J.D., 2021 · 2021
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Deep geometric representations for modeling effects of mutations on protein-protein binding affinity
Liu, X., Luo, Y., Li, P., Song, S., Peng, J., 2021 · 2021
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A structure-based b-cell epitope prediction model through combing local and global features
Lu, S., Li, Y., Nan, X., Zhang, S., 2021 · 2021
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Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Mason, D.M., Friedensohn, S., Weber, C.R., Jordi, C., Wagner, B., Meng, S.M., Ehling, R.A., Bonati, L., Dahinden, J., Gainza, P., et al., 2021 · 2021
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Exploring antibody repurposing for covid-19: Beyond presumed roles of therapeutic antibodies
Rawat, P., Sharma, D., Srivastava, A., Janakiraman, V., Gromiha, M.M., 2021 · 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., Marks, D.S., 2021 · 2021
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Generative language modeling for antibody design
Shuai, R.W., Ruffolo, J.A., Gray, J.J., 2021 · 2021
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Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
Wan, X., Nguyen, V., Ha, H., Ru, B., Lu, C., Osborne, M.A., 2021 · 2021
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Ab-ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope, in: Mabs, Taylor & Francis. p. 1873478
Wong, W.K., Robinson, S.A., Bujotzek, A., Georges, G., Lewis, A.P., Shi, J., Snowden, J., Taddese, B., Deane, C.M., 2021 · 2021
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Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness
Bachas, S., Rakocevic, G., Spencer, D., Sastry, A.V., Haile, R., Sutton, J.M., Kasun, G., Stachyra, A., Gutierrez, J.M., Yassine, E., et al., 2022 · 2022
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Bayesian Optimization
Garnett, R., 2022 · 2022
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Accelerating bayesian optimization for biological sequence design with denoising autoencoders
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