Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning
Abstract The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising antibody sequences, structures, and functi...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Journal of Biomedical Science |
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| Online Access: | https://doi.org/10.1186/s12929-025-01141-x |
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| author | Ryo Matsunaga Kouhei Tsumoto |
| author_facet | Ryo Matsunaga Kouhei Tsumoto |
| author_sort | Ryo Matsunaga |
| collection | DOAJ |
| description | Abstract The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising antibody sequences, structures, and functional properties to train predictive models that enable rational design. This review highlights the significant advancements in data acquisition and feature extraction, emphasizing the necessity of capturing both sequence and structural information. We illustrate how machine learning models, including protein language models, are used not only to enhance affinity but also to optimize other crucial therapeutic properties, such as specificity, stability, viscosity, and manufacturability. Furthermore, we provide practical examples and case studies to demonstrate how the synergy between experimental and computational approaches accelerates antibody engineering. Finally, this review discusses the remaining challenges in fully realizing the potential of artificial intelligence (AI)-powered antibody discovery pipelines to expedite therapeutic development. |
| format | Article |
| id | doaj-art-c7f3ffca30c54b1fa5b3d4f6c6adbeb7 |
| institution | OA Journals |
| issn | 1423-0127 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biomedical Science |
| spelling | doaj-art-c7f3ffca30c54b1fa5b3d4f6c6adbeb72025-08-20T01:49:35ZengBMCJournal of Biomedical Science1423-01272025-05-0132111510.1186/s12929-025-01141-xAccelerating antibody discovery and optimization with high-throughput experimentation and machine learningRyo Matsunaga0Kouhei Tsumoto1Department of Bioengineering, School of Engineering, The University of TokyoDepartment of Bioengineering, School of Engineering, The University of TokyoAbstract The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising antibody sequences, structures, and functional properties to train predictive models that enable rational design. This review highlights the significant advancements in data acquisition and feature extraction, emphasizing the necessity of capturing both sequence and structural information. We illustrate how machine learning models, including protein language models, are used not only to enhance affinity but also to optimize other crucial therapeutic properties, such as specificity, stability, viscosity, and manufacturability. Furthermore, we provide practical examples and case studies to demonstrate how the synergy between experimental and computational approaches accelerates antibody engineering. Finally, this review discusses the remaining challenges in fully realizing the potential of artificial intelligence (AI)-powered antibody discovery pipelines to expedite therapeutic development.https://doi.org/10.1186/s12929-025-01141-xAntibody therapeuticsMachine learningData-driven designAntibody designComputational antibody engineering |
| spellingShingle | Ryo Matsunaga Kouhei Tsumoto Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning Journal of Biomedical Science Antibody therapeutics Machine learning Data-driven design Antibody design Computational antibody engineering |
| title | Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning |
| title_full | Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning |
| title_fullStr | Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning |
| title_full_unstemmed | Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning |
| title_short | Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning |
| title_sort | accelerating antibody discovery and optimization with high throughput experimentation and machine learning |
| topic | Antibody therapeutics Machine learning Data-driven design Antibody design Computational antibody engineering |
| url | https://doi.org/10.1186/s12929-025-01141-x |
| work_keys_str_mv | AT ryomatsunaga acceleratingantibodydiscoveryandoptimizationwithhighthroughputexperimentationandmachinelearning AT kouheitsumoto acceleratingantibodydiscoveryandoptimizationwithhighthroughputexperimentationandmachinelearning |