Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Open accessAbstract
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 3 variants) expressed in mammalian cells, which typically results in few optimized leads. Here we show that optimized antibody variants can be identified by predicting antigen specificity via deep learning from a massively diverse space of antibody sequences. To produce data for training deep neural networks, we deep-sequenced libraries of the therapeutic antibody trastuzumab (about 1 × 10 4 variants), expressed in a mammalian cell line through site-directed mutagenesis via CRISPR-Cas9-mediated homology-directed repair, and screened the libraries for specificity to human epidermal growth factor receptor 2 (HER2). We then used the trained neural networks to screen a computational library of approximately 1 × 10 8 trastuzumab variants and predict the HER2-specific subset (approximately 1 × 10 6 variants), which can then be filtered for viscosity, clearance, solubility and immunogenicity to generate thousands of highly optimized lead candidates. Recombinant expression and experimental testing of 30 randomly selected variants from the unfiltered library showed that all 30 retained specificity for HER2. Deep learning may facilitate antibody engineering and optimization.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Mason et al. presented a method to train a convolutional neural network on the results of a directed evolution experiment to generate improved libraries.
“Mason et al. [65] presented a method to train a convolutional neural network on the results of a directed evolution experiment to generate improved libraries.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [65]
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
CRC Handbook of Chemistry and Physics
(2014) · doi:10.1201/b17118 · Cited by 57,706 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
ggplot2: Elegant Graphics for Data Analysis
Wickham, H. (2009) · doi:10.1007/978-0-387-98141-3 · Cited by 14,447 · see citing works
Wickham, H. (2016) · Use R! · doi:10.1007/978-3-319-24277-4_9 · Cited by 12,675 · see citing works
The igraph software package for complex network research
Csárdi, G., & Nepusz, T. (2006) · Medical Entomology and Zoology · Cited by 10,078 · see citing works
A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515 · see citing works
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566 · see citing works
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873 · see citing works
MiXCR: software for comprehensive adaptive immunity profiling
Bolotin, D. A., Poslavskii, S., Mitrophanov, I., Shugay, M., Mamedov, I. Z., Putintseva, E. V., & Chudakov, D. M. (2015) · Nature Methods · doi:10.1038/nmeth.3364 · Cited by 1,730 · see citing works
Structure of the extracellular region of HER2 alone and in complex with the Herceptin Fab
Cho, H., Mason, K., Ramyar, K. X., Stanley, A. M., Gabelli, S. B., Denney, D. W., & Leahy, D. J. (2003) · Nature · doi:10.1038/nature01392 · Cited by 1,615 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Boughorbel, S., Jarray, F., & El-Anbari, M. (2017) · PLOS ONE · doi:10.1371/journal.pone.0177678 · Cited by 1,222 · see citing works
Methods for the directed evolution of proteins
Packer, M. S., & Liu, D. R. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3927 · Cited by 1,002 · see citing works
ggseqlogo: a versatile R package for drawing sequence logos
Wagih, O. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx469 · Cited by 933 · see citing works
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gaínza, P., Sverrisson, F., Monti, F., Rodolà, E., Boscaini, D., Bronstein, M. M., & Correia, B. E. (2019) · Nature Methods · doi:10.1038/s41592-019-0666-6 · Cited by 811 · see citing works
Self-citationImproved methods for predicting peptide binding affinity to MHC class II molecules
Jensen, K. K., Andreatta, M., Marcatili, P., Buus, S., Greenbaum, J., Zhen, Y., Sette, A., Peters, B., & Nielsen, M. (2018) · Immunology · doi:10.1111/imm.12889 · Cited by 805 · see citing works
Biophysical properties of the clinical-stage antibody landscape
Jain, T., Sun, T., Durand, S., Hall, A., Houston, N. R., Nett, J. H., Sharkey, B., Bobrowicz, B., Caffry, I., Yu, Y., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616408114 · Cited by 629 · cited 3× · see citing works
“…plasmids, this last stage of development is done at very low throughput, as elaborate cloning, transfection and purification strategies must be implemented to screen libraries in the maximum range of 1 × 10 3 , meaning that only minor changes (for example, point mutations) are screened 5 .”
BackgroundDeep generative models of genetic variation capture the effects of mutations
Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601 · see citing works
Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018) · Nature Biotechnology · doi:10.1038/nbt.4233 · Cited by 584 · see citing works
Machine learning-assisted directed protein evolution with combinatorial libraries
Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J., & Arnold, F. H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1901979116 · Cited by 580 · see citing works
Logomaker: beautiful sequence logos in Python
Tareen, A., & Kinney, J. B. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz921 · Cited by 530 · see citing works
High-resolution mapping of protein sequence-function relationships
Fowler, D. M., Araya, C. L., Fleishman, S. J., Kellogg, E. H., Stephany, J. J., Baker, D., & Fields, S. (2010) · Nature Methods · doi:10.1038/nmeth.1492 · Cited by 516 · see citing works
The CamSol method of rational design of protein mutants with enhanced solubility
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2015) · Journal of Molecular Biology · doi:10.1016/j.jmb.2014.09.026 · Cited by 494 · see citing works
NGL viewer: web-based molecular graphics for large complexes
Rose, A. S., Bradley, A. R., Valasatava, Y., Duarte, J. M., Prlić, A., & Rose, P. W. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty419 · Cited by 489 · see citing works
Greenbaum, J., Sidney, J., Chung, J., Brander, C., Peters, B., & Sette, A. (2011) · Immunogenetics · doi:10.1007/s00251-011-0513-0 · Cited by 449 · see citing works
Whitehead, T. A., Chevalier, A. A., Song, Y., Dreyfus, C., Fleishman, S. J., de Mattos, C., Myers, C., Kamisetty, H., Blair, P. J., Wilson, I. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2214 · Cited by 364 · see citing works
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., & Deane, C. M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1810576116 · Cited by 357 · see citing works
Igawa, T., Ishii, S., Tachibana, T., Maeda, A., Higuchi, Y., Shimaoka, S., Moriyama, C., Watanabe, T., Takubo, R., Doi, Y., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1691 · Cited by 349 · see citing works
Navigating the protein fitness landscape with Gaussian processes
Romero, P. A., Krause, A., & Arnold, F. H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215251110 · Cited by 346 · see citing works
Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification
Bulik-Sullivan, B. K., Busby, J., Palmer, C. D., Davis, M. J., Murphy, T., Clark, A., Busby, M., Duke, F. F., Yang, A., Young, L., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4313 · Cited by 311 · see citing works
Vaisman‐Mentesh, A., Gutiérrez-González, M., DeKosky, B. J., & Wine, Y. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01951 · Cited by 295 · see citing works
Sharma, V. K., Patapoff, T. W., Kabakoff, B., Pai, S., Hilario, E., Zhang, B., Li, C., Borisov, O., Kelley, R. F., Chorny, I., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421779112 · Cited by 256 · see citing works
ColorBrewer in Print: A Catalog of Color Schemes for Maps
Brewer, C. A., Hatchard, G. W., & Harrower, M. (2003) · Cartography and Geographic Information Science · doi:10.1559/152304003100010929 · Cited by 230 · see citing works
Tomar, D. S., Kumar, S., Singh, S. K., Goswami, S., & Li, L. (2016) · mAbs · doi:10.1080/19420862.2015.1128606 · Cited by 215 · see citing works
Cuperus, J. T., Groves, B., Kuchina, A., Rosenberg, A. B., Jojic, N., Fields, S., & Seelig, G. (2017) · Genome Research · doi:10.1101/gr.224964.117 · Cited by 205 · see citing works
Antidrug Antibody Formation in Oncology: Clinical Relevance and Challenges
van Brummelen, E. M. J., Ros, W., Wolbink, G., Beijnen, J. H., & Schellens, J. H. M. (2016) · The Oncologist · doi:10.1634/theoncologist.2016-0061 · Cited by 150 · see citing works
Slaga, D., Ellerman, D. A., Lombana, T. N., Vij, R., Li, J., Hristopoulos, M. L., Clark, R., Johnston, J., Shelton, A., Mai, E., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aat5775 · Cited by 138 · see citing works
Large-scale network analysis reveals the sequence space architecture of antibody repertoires
Miho, E., Roškar, R., Greiff, V., & Reddy, S. T. (2019) · Nature Communications · doi:10.1038/s41467-019-09278-8 · Cited by 138 · see citing works
Self-citationThird generation antibody discovery methods: in silico rational design
Sormanni, P., Aprile, F. A., & Vendruscolo, M. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00523k · Cited by 136 · see citing works
Vajdos, F. F., Adams, C. W., Breece, T., Presta, L. G., de Vos, A. M., & Sidhu, S. S. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00264-4 · Cited by 118 · see citing works
Greiff, V., Weber, C. R., Palme, J., Bodenhofer, U., Miho, E., Menzel, U., & Reddy, S. T. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1700594 · Cited by 113 · see citing works
Self-citationBedbrook, C. N., Yang, K., Rice, A. J., Gradinaru, V., & Arnold, F. H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005786 · Cited by 112 · see citing works
Analyzing the ‘Degree of Humanness’ of antibody sequences
Abhinandan, K. R., & Martin, A. C. R. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.02.100 · Cited by 99 · see citing works
In vitro and in silico assessment of the developability of a designed monoclonal antibody library
Wolf Pérez, A.-M., Sormanni, P., Andersen, J. S., Sakhnini, L. I., Rodriguez-Leon, I., Bjelke, J. R., Gajhede, A. J., De Maria, L., Otzen, D. E., Vendruscolo, M., et al. (2019) · mAbs · doi:10.1080/19420862.2018.1556082 · Cited by 97 · see citing works
Antidrug Antibodies in Patients Treated with Alirocumab
Roth, E. M., Goldberg, A. C., Catapano, A. L., Torri, A., Yancopoulos, G. D., Stahl, N., Brunet, A., Lecorps, G., & Colhoun, H. M. (2017) · New England Journal of Medicine · doi:10.1056/nejmc1616623 · Cited by 90 · see citing works
Kang, J. C., Sun, W., Khare, P., Karimi, M., Wang, X., Shen, Y., Ober, R. J., & Ward, E. S. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0073-7 · Cited by 90 · see citing works
Hu, D., Hu, S., Wan, W., Xu, M., Du, R., Zhao, W., Gao, X., Liu, J., Liu, H., & Hong, J. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129125 · Cited by 86 · cited 1× · see citing works
“…has been discovered, additional engineering is often required; phage and yeast display offer a powerful method for high-throughput screening of large mutagenesis libraries ( > 1 × 10 9 ); however, they are primarily used only for increasing affinity or specificity to the target antigen 4 .”
MethodMason, D. M., Weber, C. R., Parola, C., Meng, S. M., Greiff, V., Kelton, W., & Reddy, S. T. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky550 · Cited by 83 · see citing works
Self-citationNet charge of antibody complementarity-determining regions is a key predictor of specificity
Rabia, L. A., Zhang, Y., Ludwig, S. D., Julian, M. C., & Tessier, P. M. (2018) · Protein Engineering Design and Selection · doi:10.1093/protein/gzz002 · Cited by 81 · see citing works
Optimizing the search algorithm for protein engineering by directed evolution
Fox, R. I., Roy, A., Govindarajan, S., Minshull, J., Gustafsson, C., Jones, J. T., & Emig, R. (2003) · Protein Engineering Design and Selection · doi:10.1093/protein/gzg077 · Cited by 73 · see citing works
Directed molecular evolution by machine learning and the influence of nonlinear interactions
Fox, R. J. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.11.031 · Cited by 65 · see citing works
Immunogenomic engineering of a plug-and-(dis)play hybridoma platform
Pogson, M., Parola, C., Kelton, W., Heuberger, P., & Reddy, S. T. (2016) · Nature Communications · doi:10.1038/ncomms12535 · Cited by 48 · see citing works
Self-citationMenzel, U., Greiff, V., Khan, T. A., Haessler, U., Hellmann, I., Friedensohn, S., Cook, S. C., Pogson, M., & Reddy, S. T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096727 · Cited by 47 · see citing works
Self-citationInnovation by homologous recombination
Trudeau, D. L., Smith, M. A., & Arnold, F. H. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2013.10.007 · Cited by 36 · see citing works
Development of a semi-automated high throughput transient transfection system
Bos, A. B., Duque, J. N., Bhakta, S., Farahi, F., Chirdon, L. A., Junutula, J. R., Harms, P. D., & Wong, A. W. (2014) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2014.03.027 · Cited by 31 · see citing works
Townsend, S., Fennell, B. J., Apgar, J. R., Lambert, M. A., McDonnell, B., Grant, J., Wade, J. M., Franklin, E., Foy, N., Shúilleabháin, D. N., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510944112 · Cited by 26 · see citing works
Machine Learning Methods for Disease Prediction with Claims Data
Christensen, T., Frandsen, A., Glazier, S., Humpherys, J., & Kartchner, D. (2018) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2018.00108 · Cited by 24 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Efficient evolution of human antibodies from general protein language models
Hie, B., Shanker, V. R., Xu, D., Bruun, T. U. J., Weidenbacher, P. A. B., Tang, S., Wu, W. C. H., Pak, J. E., & Kim, P. S. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01763-2 · Cited by 444 · see citing works
Machine learning for functional protein design
Notin, P., Rollins, N. J., Gal, Y., Sander, C., & Marks, D. S. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02127-0 · Cited by 278 · see citing works
Designing antibodies as therapeutics
Carter, P. J., & Rajpal, A. (2022) · Cell · doi:10.1016/j.cell.2022.05.029 · Cited by 275 · see citing works
Prihoda, D., Maamary, J., Waight, A., Juan, V., Fayadat-Dilman, L., Svozil, D., & Bitton, D. A. (2022) · mAbs · doi:10.1080/19420862.2021.2020203 · Cited by 190 · see citing works
Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
Next-generation agentic AI for transforming healthcare
Karunanayake, N. (2025) · Informatics and Health · doi:10.1016/j.infoh.2025.03.001 · Cited by 165 · see citing works
Makowski, E. K., Kinnunen, P. C., Huang, J., Wu, L., Smith, M. D., Wang, T., Desai, A. A., Streu, C. N., Zhang, Y., Zupancic, J. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31457-3 · Cited by 155 · see citing works
Deciphering the language of antibodies using self-supervised learning
Leem, J., Mitchell, L., Farmery, J. H. R., Barton, J., & Galson, J. D. (2022) · Patterns · doi:10.1016/j.patter.2022.100513 · Cited by 154 · see citing works
Accelerated rational PROTAC design via deep learning and molecular simulations
Zheng, S., Tan, Y., Wang, Z., Li, C., Zhang, Z., Sang, X., Chen, H., & Yang, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00527-y · Cited by 145 · see citing works
Lin, M., Guo, J., Gu, Z., Tang, W., Tao, H., You, S., Jia, D., Sun, Y., & Jia, P. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06425-2 · Cited by 158 · see citing works
Development of therapeutic antibodies for the treatment of diseases
Wang, Z., Wang, G., Lu, H., Li, H., Tang, M., & Tong, A. (2022) · Molecular Biomedicine · doi:10.1186/s43556-022-00100-4 · Cited by 136 · see citing works
Collin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., Salehzadeh‐Yazdi, A., Kirschner, M. W., Krobitsch, S., & Kuepfer, L. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020166 · Cited by 122 · see citing works
Design of Biopharmaceutical Formulations Accelerated by Machine Learning
Narayanan, H., Dingfelder, F., Morales, I. C., Patel, B., Heding, K. E., Bjelke, J. R., Egebjerg, T., Butté, A., Sokolov, M., Lorenzen, N., et al. (2021) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.1c00469 · Cited by 117 · see citing works
Akbar, R., Bashour, H., Rawat, P., Robert, P. A., Smorodina, E., Cotet, T., Karlsen, K. F., Frank, R., Mehta, B. B., Vu, M. H., et al. (2022) · mAbs · doi:10.1080/19420862.2021.2008790 · Cited by 117 · see citing works
Animal-derived products in science and current alternatives
Duarte, A. C., Costa, E. C., Filipe, H. A. L., Saraiva, S. M., Jacinto, T., Miguel, S. P., Ribeiro, M. P., & Coutinho, P. (2023) · Biomaterials Advances · doi:10.1016/j.bioadv.2023.213428 · Cited by 111 · see citing works
Taft, J. M., Weber, C. R., Gao, B., Ehling, R. A., Han, J., Frei, L., Metcalfe, S. W., Overath, M. D., Yermanos, A., Kelton, W., et al. (2022) · Cell · doi:10.1016/j.cell.2022.08.024 · Cited by 113 · see citing works
Self-citationDLAB-deep learning methods for structure-based virtual screening of antibodies
Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102 · see citing works
Li, L., Gupta, E., Spaeth, J., Shing, L., Jaimes, R., Engelhart, E., Lopez, R., Caceres, R. S., Bepler, T., & Walsh, M. E. (2023) · Nature Communications · doi:10.1038/s41467-023-39022-2 · Cited by 96 · see citing works
Directed Evolution of Aptamer Discovery Technologies
Wu, D., Gordon, C. K. L., Shin, J. H., Eisenstein, M. S., & Soh, H. T. (2022) · Accounts of Chemical Research · doi:10.1021/acs.accounts.1c00724 · Cited by 94 · see citing works
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) · mAbs · doi:10.1080/19420862.2022.2031482 · Cited by 94 · see citing works
Self-citationThe immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires
Pavlović, M., Scheffer, L., Motwani, K., Kanduri, C., Kompova, R., Vazov, N., Waagan, K., Bernal, F. L. M., da Costa, A. A., Corrie, B., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00413-z · Cited by 89 · see citing works
Self-citationUnlocking de novo antibody design with generative artificial intelligencePreprint
Shanehsazzadeh, A., McPartlon, M., Kasun, G. W., Steiger, A. K., Sutton, J. M., Yassine, E., McCloskey, C. M., Haile, R. G., Shuai, R. W., Alverio, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.08.523187 · Cited by 83 · see citing works
Non-specificity as the sticky problem in therapeutic antibody development
Ausserwöger, H., Schneider, M. M., Herling, T. W., Arosio, P., Invernizzi, G., Knowles, T. P. J., & Lorenzen, N. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00438-x · Cited by 80 · see citing works
Wilman, W., Wróbel, S., Bielska, W., Deszyński, P., Dudzic, P., Jaszczyszyn, I., Kaniewski, J., Młokosiewicz, J., Rouyan, A., Satława, T., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac267 · Cited by 83 · see citing works
Cunningham, O., Scott, M. J., Zhou, Z., & Finlay, W. J. J. (2021) · mAbs · doi:10.1080/19420862.2021.1999195 · Cited by 72 · see citing works
Antibody variable region engineering for improving cancer immunotherapy
Lou, H., & Cao, X. (2022) · 癌症：英文版 · doi:10.1002/cac2.12330 · Cited by 75 · see citing works
Systematic multi-trait AAV capsid engineering for efficient gene delivery
Eid, F.-E., Chen, A. T., Chan, K. Y., Huang, Q., Zheng, Q., Tobey, I. G., Pacouret, S., Brauer, P. P., Keyes, C., Powell, M. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50555-y · Cited by 74 · see citing works
Custom CRISPR–Cas9 PAM variants via scalable engineering and machine learning
Silverstein, R. A., Kim, N., Kroell, A.-S. H., Walton, R. T., Delano, J., Butcher, R. M., Pačesa, M., Smith, B., Christie, K. A., Ha, L. L., et al. (2025) · Nature · doi:10.1038/s41586-025-09021-y · Cited by 77 · see citing works
Self-citationDe novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model
He, H.-H., He, B., Guan, L., Zhao, Y., Jiang, F., Chen, G.-X., Zhu, Q., Chen, C., Li, T., & Yao, J.-H. (2024) · Nature Communications · doi:10.1038/s41467-024-50903-y · Cited by 73 · see citing works
Saitō, Y., Oikawa, M., Sato, T., Nakazawa, H., Ito, T., Kameda, T., Tsuda, K., & Umetsu, M. (2021) · ACS Catalysis · doi:10.1021/acscatal.1c03753 · Cited by 72 · see citing works
Predicting the antigenic evolution of SARS-COV-2 with deep learning
Han, W., Chen, N., Xu, X., Sahil, A., Zhou, J., Li, Z., Zhong, H., Gao, E., Zhang, R., Wang, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39199-6 · Cited by 64 · see citing works
A potent pan-sarbecovirus neutralizing antibody resilient to epitope diversification
Rosen, L. E., Tortorici, M. A., Marco, A. D., Pinto, D., Foreman, W. B., Taylor, A. L., Park, Y., Bohan, D., Rietz, T. A., Errico, J. M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.026 · Cited by 66 · see citing works
Robert, P. A., Akbar, R., Frank, R., Pavlović, M., Widrich, M., Snapkov, I., Slabodkin, A., Chernigovskaya, M., Scheffer, L., Smorodina, E., et al. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00372-4 · Cited by 64 · see citing works
Artificial intelligence-driven drug development against autoimmune diseases
Moingeon, P. E. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2023.04.005 · Cited by 66 · see citing works
Bachas, S. T., Rakocevic, G., Spencer, D., Sastry, A. V., Haile, R. G., Sutton, J. M., Kasun, G. W., Stachyra, A. L., Gutierrez, J. M., Yassine, E., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.16.504181 · Cited by 63 · see citing works
Li, T., Li, Y., Zhu, X., He, Y., Wu, Y., Ying, T., & Xie, Z. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.007 · Cited by 59 · see citing works
Adaptive immune receptor repertoire analysis
Mhanna, V., Bashour, H., Quý, K. L., Barennes, P., Rawat, P., Greiff, V., & Mariotti‐Ferrandiz, E. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-023-00284-1 · Cited by 62 · see citing works
Generative models for protein sequence modeling: recent advances and future directions
Mardikoraem, M., Wang, Z., Pascual, N., & Woldring, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad358 · Cited by 57 · see citing works
Bai, G., Sun, C., Guo, Z., Wang, Y., Zeng, X., Su, Y., Zhao, Q., & Ma, B. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.06.005 · Cited by 56 · see citing works
Dewaker, V., Morya, V. K., Kim, Y. H., Park, S. T., Kim, H. S., & Koh, Y. H. (2025) · Biomarker Research · doi:10.1186/s40364-025-00764-4 · Cited by 59 · see citing works
Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2
Zhang, Y., Ye, T., Xi, H., Juhas, M., & Li, J. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.739684 · Cited by 53 · see citing works
Hummer, A. M., Schneider, C., Chinery, L., & Deane, C. M. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00823-8 · Cited by 53 · see citing works
Antibody design using deep learning: from sequence and structure design to affinity maturation
Joubbi, S., Micheli, A., Milazzo, P., Maccari, G., Ciano, G., Cardamone, D., & Medini, D. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae307 · Cited by 54 · see citing works
Monoclonal antibodies: From magic bullet to precision weapon
Aboul-Ella, H., Gohar, A., Ali, A. A., Ismail, L. M., Mahmoud, A. E. E.-R., Elkhatib, W. F., & Aboul-Ella, H. (2024) · Molecular Biomedicine · doi:10.1186/s43556-024-00210-1 · Cited by 55 · see citing works
Progress and challenges in mass spectrometry-based analysis of antibody repertoires
Snapkov, I., Chernigovskaya, M., Sinitcyn, P. G., Quý, K. L., Nyman, T. A., & Greiff, V. (2021) · Trends in biotechnology · doi:10.1016/j.tibtech.2021.08.006 · Cited by 50 · see citing works
Deep mutational scanning for therapeutic antibody engineering
Hanning, K. R., Minot, M., Warrender, A. K., Kelton, W., & Reddy, S. T. (2021) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2021.11.010 · Cited by 51 · see citing works
Self-citationThe RESP AI model accelerates the identification of tight-binding antibodies
Parkinson, J., Hard, R. L., & Wang, W. (2023) · Nature Communications · doi:10.1038/s41467-023-36028-8 · Cited by 50 · see citing works
Predicting antibody binders and generating synthetic antibodies using deep learning
Lim, Y. W., Adler, A. S., & Johnson, D. S. (2022) · mAbs · doi:10.1080/19420862.2022.2069075 · Cited by 49 · see citing works
Accelerating therapeutic protein design with computational approaches toward the clinical stage
Chen, Z., Wang, X., Chen, X., Huang, J., Wang, C., Wang, J., & Wang, Z. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.04.027 · Cited by 49 · see citing works
Makowski, E. K., Wang, T., Zupancic, J. M., Huang, J., Wu, L., Schardt, J. S., Groot, A. S. D., Elkins, S. L., Martin, W. D., & Tessier, P. M. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01074-6 · Cited by 49 · see citing works
Artificial intelligence for dementia drug discovery and trials optimization
Doherty, T., Yao, Z., Khleifat, A. A. L., Tantiangco, H., Tamburin, S., Albertyn, C., Thakur, L., Llewellyn, D. J., Oxtoby, N. P., Lourida, I., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13428 · Cited by 48 · see citing works
Toward real-world automated antibody design with combinatorial Bayesian optimization
Khan, A. A., Cowen-Rivers, A. I., Grosnit, A., Deik, D.-G.-X., Robert, P. A., Greiff, V., Smorodina, E., Rawat, P., Akbar, R., Dreczkowski, K., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100374 · Cited by 54 · see citing works
Harmalkar, A., Rao, R., Xie, Y. R., Honer, J., Deisting, W., Anlahr, J., Hoenig, A., Czwikla, J., Sienz-Widmann, E., Rau, D., et al. (2023) · mAbs · doi:10.1080/19420862.2022.2163584 · Cited by 48 · see citing works
Li, J., Kang, G., Wang, J., Yuan, H., Wu, Y., Meng, S., Wang, P., Zhang, M., Wang, Y., Feng, Y., et al. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.125733 · Cited by 48 · see citing works
Self-citationThe evolutionary and functional significance of germline immunoglobulin gene variation
Pennell, M., Rodriguez, O. L., Watson, C. T., & Greiff, V. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.11.001 · Cited by 44 · see citing works
Technological advancements in antibody-based therapeutics for treatment of diseases
Lu, R., Chiang, H.-L., Yuan, J. P., Wang, H.-H., Chen, C.-Y., Panda, S. S., Liang, K.-H., Peng, H., Ko, S.-H., Hsu, H.-J., et al. (2025) · Journal of Biomedical Science · doi:10.1186/s12929-025-01190-2 · Cited by 53 · see citing works
Design of Protein Segments and Peptides for Binding to Protein Targets
Gupta, S., Azadvari, N., & Hosseinzadeh, P. (2022) · BioDesign Research · doi:10.34133/2022/9783197 · Cited by 45 · see citing works
Madsen, A. V., Mejias-Gomez, O., Pedersen, L. E., Morth, J. P., Kristensen, P. K., Jenkins, T. P., & Goletz, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.11.056 · Cited by 45 · see citing works
Thean, D., Chu, H. Y., Fong, J. H. C., Chan, B. K. C., Zhou, P., Kwok, C. C. S., Chan, Y. M., Mak, S. Y. L., Choi, G. C. G., Ho, J. W. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29874-5 · Cited by 42 · see citing works
Lai, P. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.04.035 · Cited by 39 · see citing works
Applications of Machine and Deep Learning in Adaptive Immunity
Pertseva, M., Gao, B., Neumeier, D., Yermanos, A., & Reddy, S. T. (2021) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-101420-125021 · Cited by 38 · see citing works
Self-citationPredicting unseen antibodies’ neutralizability via adaptive graph neural networks
Zhang, J., Du, Y., Zhou, P., Ding, J., Xia, S., Wang, Q., Chen, F., Zhou, M., Zhang, X., Wang, W.-F., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00553-w · Cited by 37 · see citing works
Svilenov, H. L., Arosio, P., Menzen, T., Tessier, P. M., & Sormanni, P. (2023) · mAbs · doi:10.1080/19420862.2022.2164459 · Cited by 39 · see citing works
Training data composition determines machine learning generalization and biological rule discovery
Ursu, E., Minnegalieva, A. R., Rawat, P., Chernigovskaya, M., Tăcutu, R., Sandve, G. K., Robert, P. A., & Greiff, V. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01089-5 · Cited by 36 · see citing works
Qin, H., Zhou, Z., Shi, R., Mai, Y., Xu, Y., Fu, P., Cheng, G., Zhang, Z., Chen, W., Chen, Y., et al. (2025) · Journal of Hematology & Oncology · doi:10.1186/s13045-025-01701-6 · Cited by 41 · see citing works
Meta learning addresses noisy and under-labeled data in machine learning-guided antibody engineering
Minot, M., & Reddy, S. T. (2024) · Cell Systems · doi:10.1016/j.cels.2023.12.003 · Cited by 35 · see citing works
Self-citationAccurate prediction of antibody function and structure using bio-inspired antibody language model
Jing, H., Gao, Z., Xu, S. Y., Shen, T., Peng, Z., He, S., You, T., Ye, S., Lin, W. J., & Sun, S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae245 · Cited by 34 · see citing works
da Silva, F. L. B., Giron, C. C., & Laaksonen, A. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04225 · Cited by 30 · see citing works
Kim, D. N., McNaughton, A. D., & Kumar, N. (2024) · Bioengineering · doi:10.3390/bioengineering11020185 · Cited by 34 · see citing works
Slavny, P., Hegde, M., Doerner, A., Parthiban, K., McCafferty, J., Zielonka, S., & Hoet, R. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1469329 · Cited by 33 · see citing works
Xu, X., Xu, T., Zhou, J., Liao, X., Zhang, R., Wang, Y., Zhang, L., & Gao, X. (2023) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.03.004 · Cited by 32 · see citing works
Bashour, H., Smorodina, E., Pariset, M., Zhong, J., Akbar, R., Chernigovskaya, M., Lê Quý, K., Snapkow, I., Rawat, P., Krawczyk, K., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06561-3 · Cited by 32 · see citing works
The Application of Machine Learning on Antibody Discovery and Optimization
Zheng, J., Wang, Y., Liang, Q., Cui, L., & Wang, L. (2024) · Molecules · doi:10.3390/molecules29245923 · Cited by 34 · see citing works
Raffele, D. D., & Ilie, I. M. (2023) · Chemical Communications · doi:10.1039/d3cc04630c · Cited by 32 · see citing works
Liu, Y., Zhu, K., Peng, W., Liu, Z., & Mao, X. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02631-6 · Cited by 35 · see citing works
Integrating Computational Design and Experimental Approaches for Next-Generation Biologics
Son, A., Park, J., Kim, W., Lee, W., Yoon, Y., Ji, J., & Kim, H. (2024) · Biomolecules · doi:10.3390/biom14091073 · Cited by 29 · see citing works
Matsunaga, R., & Tsumoto, K. (2025) · Journal of Biomedical Science · doi:10.1186/s12929-025-01141-x · Cited by 30 · see citing works
Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics
Asada, K., Komatsu, M., Shimoyama, R., Takasawa, K., Shinkai, N., Sakai, A., Bolatkan, A., Yamada, M., Takahashi, S., Machino, H., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090886 · Cited by 29 · see citing works
D’Orsi, L., Capasso, B., Lamacchia, G., Pizzichini, P., Ferranti, S., Liverani, A., Fontana, C., Panunzi, S., Gaetano, A. D., & Presti, E. L. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252111588 · Cited by 27 · see citing works
Enhancing Antibody Language Models with Structural InformationPreprint
Barton, J., Galson, J. D., & Leem, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.12.569610 · Cited by 29 · see citing works
DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability
Kalejaye, L. A., Wu, I.-E., Terry, T., & Lai, P. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.05.029 · Cited by 26 · see citing works
When Weak Is Strong: A Plea for Low‐Affinity Binders for Optical Microscopy
Albertazzi, L., & Heilemann, M. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202303390 · Cited by 26 · see citing works
Ito, T., Nguyen, T. D., Saitō, Y., Kurumida, Y., Nakazawa, H., Kawada, S., Nishi, H., Tsuda, K., Kameda, T., & Umetsu, M. (2023) · mAbs · doi:10.1080/19420862.2023.2168470 · Cited by 25 · see citing works
Arras, P., Yoo, H. B., Pekar, L., Clarke, T., Friedrich, L., Schröter, C. J., Schanz, J., Tonillo, J., Siegmund, V., Doerner, A., et al. (2023) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2023.1249247 · Cited by 25 · see citing works
AI-driven antibody design with generative diffusion models: current insights and future directions
He, X., Li, J.-R., Xu, J., Shan, H., Shen, S.-Y., Gao, S.-H., & Xu, H. E. (2024) · Acta Pharmacologica Sinica · doi:10.1038/s41401-024-01380-y · Cited by 25 · see citing works
Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning
O’Donnell, T. J., Kanduri, C., Isacchini, G., Limenitakis, J. P., Brachman, R. A., Alvarez, R. A., Haff, I. H., Sandve, G. K., & Greiff, V. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.006 · Cited by 22 · see citing works
Insights into next generation sequencing guided antibody selection strategies
Erasmus, M. F., Ferrara, F., D'Angelo, S., Spector, L., Leal-Lopes, C., Teixeira, A. A. R., Sørensen, J., Nagpal, S., Perea-Schmittle, K., Choudhary, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-45538-w · Cited by 25 · see citing works
Deep learning in preclinical antibody drug discovery and development
Zhou, Y., Huang, Z., Li, W., Wei, J., Jiang, Q., Yang, W., & Huang, J. H. (2023) · Methods · doi:10.1016/j.ymeth.2023.07.003 · Cited by 25 · see citing works
High-throughput analysis system of interaction kinetics for data-driven antibody design
Matsunaga, R., Ujiie, K., Inagaki, M., Pérez, J. F., Yasuda, Y., Mimasu, S., Soga, S., & Tsumoto, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-46756-y · Cited by 23 · see citing works
Protease engineering: Approaches, tools, and emerging trends
Martinusen, S. G., Nelson, S. E., Slaton, E. W., Long, L. F., Pho, R., Ajayebi, S., & Denard, C. A. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108602 · Cited by 23 · see citing works
Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design
Clifton, B. E., Kozome, D., & Laurino, P. (2022) · Biochemistry · doi:10.1021/acs.biochem.1c00757 · Cited by 21 · see citing works
Clark, T., Subramanian, V., Jayaraman, A., Fitzpatrick, E., Gopal, R., Pentakota, N., Rurak, T. T., Anand, S., Viglione, A., Raman, R., et al. (2023) · Communications Chemistry · doi:10.1038/s42004-023-01037-7 · Cited by 27 · see citing works
Chinery, L., Hummer, A. M., Mehta, B. B., Akbar, R., Rawat, P., Slabodkin, A., Quý, K. L., Lund‐Johansen, F., Greiff, V., Jeliazkov, J. R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.26.586756 · Cited by 22 · see citing works
Harnessing computational technologies to facilitate antibody–drug conjugate development
Croitoru, A., Orr, A. A., & MacKerell, A. D. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-01950-z · Cited by 21 · see citing works
Prospective Artificial Intelligence to Dissect the Dengue Immune Response and Discover Therapeutics
Natali, E. N., Babrak, L. M., & Miho, E. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.574411 · Cited by 21 · see citing works
Ehling, R. A., Weber, C. R., Mason, D. M., Friedensohn, S., Wagner, B., Bieberich, F., Kapetanovic, E., Vazquez-Lombardi, R., Roberto, R. B. D., Hong, K.-L., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110242 · Cited by 21 · see citing works
Self-citationPredicting adaptive immune receptor specificities by machine learning is a data generation problem.
Mason, D. M., & Reddy, S. T. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.008 · Cited by 21 · see citing works
Self-citationWu, I.-E., Kalejaye, L. A., & Lai, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c00804 · Cited by 20 · see citing works
Hallucinating structure-conditioned antibody libraries for target-specific binders
Mahajan, S. P., Ruffolo, J. A., Frick, R., & Gray, J. J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.999034 · Cited by 20 · see citing works
AbDiver-a tool to explore the natural antibody landscape to aid therapeutic design
Młokosiewicz, J., Deszyński, P., Wilman, W., Jaszczyszyn, I., Ganesan, R., Kovaltsuk, A., Leem, J., Galson, J. D., & Krawczyk, K. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac151 · Cited by 19 · see citing works
- Authors
- Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A., Bonati, L., Dahinden, J., Gainza, P., Correia, B. E., & Reddy, S. T.
- Affiliations
- ETH Zurich (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Reddy, Sai T.
- ORCID
- Venue
- Nature Biomedical Engineering
- Published
- 2021-04-15
- Volume
- 5
- Issue
- 6
- Pages
- 600-612
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W3156522942
- Cited by
- 308
- References
- 59
- Citations by year
- 2026: 55 · 2025: 66 · 2024: 62 · 2023: 60 · 2022: 48 · 2021: 17
- FWCI
- 24.67
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- Nature Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- NCCR Molecular Systems Engineering
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- therapeutic antibodies, antibody engineering, trastuzumab, HER2, deep learning, antibody optimization
- MeSH terms
- Trastuzumab, Deep Learning, Amino Acid Sequence, Animals, Antibody Affinity, Antibody Specificity, Antigens, Humans, Hybridomas, Protein Binding, Protein Engineering, Mutagenesis, Site-Directed, Erb-b2 Receptor Tyrosine Kinases, Sequence Analysis, Protein, Recombinational DNA Repair, CRISPR-Cas Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
