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Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine.
Scoring function for automated assessment of protein structure template quality
Zhang, Y. and Skolnick, J · 2004
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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OptCDR: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding
Pantazes, R. and Maranas, C. D · 2010
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RosettaRemodel: a generalized framework for flexible backbone protein design
Huang, P.-S., Ban, Y.-E. A., Richter, F., Andre, I., Vernon, R., Schief, W. R., and Baker, D · 2011
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Rosetta3: An object-oriented software suite for the simulation and design of macromolecules , pp. 545–574
Leaver-Fay, A., Tyka, M., Lewis, S., Lange, O., Thompson, J., Jacak, R., Kaufman, K., Renfrew, P., Smith, C., Sheffler, W., Davis, I., Cooper, S., Treuille, A., Mandell, D., Richter, F., Ban, Y., Fleishman, S., Corn, J., Kim, D., Lyskov, S., Berrondo, M., Mentzer, S., Popović, Z., Havranek, J., Karanicolas, J., Das, R., Meiler, J., Kortemme, T., Gray, J., Kuhlman, B., Baker, D., and Bradley, P · 2011
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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 · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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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
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Uniref clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., and Consortium, U · 2015
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The origin of CDR H3 structural diversity
Weitzner, B. D., Dunbrack Jr, R. L., and Gray, J. J · 2015
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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How many protein sequences fold to a given structure? A coevolutionary analysis
Tian, P. and Best, R. B · 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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Generative modeling for protein structures
Anand, N. and Huang, P · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Adversarial reprogramming of neural networks
Elsayed, G. F., Goodfellow, I., and Sohl-Dickstein, J · 2018
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Design of metalloproteins and novel protein folds using variational autoencoders
Greener, J. G., Moffat, L., and Jones, D. T · 2018
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Universal language model fine-tuning for text classification
Howard, J. and Ruder, S · 2018
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Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., and Krawczyk, K · 2018
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Clustering huge protein sequence sets in linear time
Steinegger, M. and Söding, J · 2018
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Computational protein design with deep learning neural networks
Wang, J., Cao, H., Zhang, J. Z., and Qi, Y · 2018
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Parameter-efficient transfer learning for NLP
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S · 2019
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Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
Cited alongside, same era.
Five computational developability guidelines for therapeutic antibody profiling
Raybould, M. I. J., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A., Shi, J., and Deane, C. M · 2019
Cited alongside, same era.
Grammar of protein domain architectures
Yu, L., Tanwar, D. K., Penha, E. D. S., Wolf, Y. I., Koonin, E. V., and Basu, M. K · 2019
Cited alongside, same era.
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
Cited alongside, same era.
IG-VAE: Generative modeling of immunoglobulin proteins by direct 3d coordinate generation
Eguchi, R. R., Anand, N., Choe, C. A., and Huang, P.-S · 2020
Cited alongside, same era.
CoV-AbDab: the Coronavirus Antibody Database
Raybould, M. I. J., Kovaltsuk, A., Marks, C., and Deane, C. M · 2021
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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 · 2021
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Recent Advances in Language Model Fine-tuning
Ruder, 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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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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ProtTrans: Towards cracking the language of life’s code through self-supervised deep learning and high performance computing
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., BHOWMIK, D., and Rost, B · 2020
Cited alongside, same era.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 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.
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.
Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources
Tsai, Y.-Y., Chen, P.-Y., and Ho, T.-Y · 2020
Cited alongside, same era.
Reprogramming language models for molecular representation learning
Vinod, R., Chen, P.-Y., and Das, P · 2020
Cited alongside, same era.
Voice2Series: Reprogramming acoustic models for time series classification
Yang, C.-H. H., Tsai, Y.-Y., and Chen, P.-Y · 2021
Later among the works it cites.
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
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Anand, N. and Achim, T · 2022
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Model reprogramming: Resource-efficient cross-domain machine learning
Chen, P.-Y · 2022
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Antibody complementarity determining regions (CDRs) design using constrained energy model
Fu, T. and Sun, J · 2022
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Emerging new therapeutic antibody derivatives for cancer treatment
Jin, S., Sun, Y., Liang, X., Gu, X., Ning, J., Xu, Y., Chen, S., and Pan, L · 2022
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GitHub repository for Iterative refinement graph neural network for antibody sequence-structure co-design (RefineGNN), 2022
Jin, W · 2022
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AntBO: Towards real-world automated antibody design with combinatorial bayesian optimisation
Khan, A., Cowen-Rivers, A. I., Deik, D.-G.-X., Grosnit, A., Dreczkowski, K., Robert, P. A., Greiff, V., Tutunov, R., Bou-Ammar, D., Wang, J., et al · 2022
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Conditional antibody design as 3D equivariant graph translation
Kong, X., Huang, W., and Liu, Y · 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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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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Cross-modal adversarial reprogramming
Neekhara, P., Hussain, S., Du, J., Dubnov, S., Koushanfar, F., and McAuley, J · 2022
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ProGen2: exploring the boundaries of protein language models
Nijkamp, E., Ruffolo, J., Weinstein, E. N., Naik, N., and Madani, A · 2022
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Observed antibody space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences
Olsen, T. H., Boyles, F., and Deane, C. M · 2022
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Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
Ruffolo, J. A., Chu, L.-S., Mahajan, S. P., and Gray, J. J · 2022
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Deep learning of protein sequence design of protein-protein interactions
Syrlybaeva, R. and Strauch, E.-M · 2022
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Ablang: An antibody language model for completing antibody sequences
Tobias H. Olsen, I. H. M. and Deane, C. M · 2022
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