Artificial intelligence-driven computational methods for antibody design and optimization
Antibodies play a crucial role in our immune system. Their ability to bind to and neutralize pathogens opens opportunities to develop antibodies for therapeutic and diagnostic use. Computational methods capable of designing antibodies for a target antigen can revolutionize drug discovery, reducing t...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | mAbs |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2025.2528902 |
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| _version_ | 1849317754190954496 |
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| author | Luiz Felipe Vecchietti Bryan Nathanael Wijaya Azamat Armanuly Begench Hangeldiyev Hyunkyu Jung Sooyeon Lee Meeyoung Cha Ho Min Kim |
| author_facet | Luiz Felipe Vecchietti Bryan Nathanael Wijaya Azamat Armanuly Begench Hangeldiyev Hyunkyu Jung Sooyeon Lee Meeyoung Cha Ho Min Kim |
| author_sort | Luiz Felipe Vecchietti |
| collection | DOAJ |
| description | Antibodies play a crucial role in our immune system. Their ability to bind to and neutralize pathogens opens opportunities to develop antibodies for therapeutic and diagnostic use. Computational methods capable of designing antibodies for a target antigen can revolutionize drug discovery, reducing the time and cost required for drug development. Artificial intelligence (AI) methods have recently achieved remarkable advancements in the design of protein sequences and structures, including the ability to generate scaffolds for a given motif and binders for a specific target. These generative methods have been applied to antigen-conditioned antibody design, with experimental binding confirmed for de novo-designed antibodies. This review surveys current AI methods used in antibody development, focusing on those for antigen-conditioned antibody design. The results obtained by AI-based methodologies in antibody and protein research suggest a promising direction for generating de novo binders for various target antigens. |
| format | Article |
| id | doaj-art-4fbedefc01c5490497bb5c02227cba84 |
| institution | Kabale University |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-4fbedefc01c5490497bb5c02227cba842025-08-20T03:51:08ZengTaylor & Francis GroupmAbs1942-08621942-08702025-12-0117110.1080/19420862.2025.2528902Artificial intelligence-driven computational methods for antibody design and optimizationLuiz Felipe Vecchietti0Bryan Nathanael Wijaya1Azamat Armanuly2Begench Hangeldiyev3Hyunkyu Jung4Sooyeon Lee5Meeyoung Cha6Ho Min Kim7Max Planck Institute for Security and Privacy (MPI-SP), Universitätsstraße 140, Bochum, GermanySchool of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaGraduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaRobotics Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaSchool of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaDepartment of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaMax Planck Institute for Security and Privacy (MPI-SP), Universitätsstraße 140, Bochum, GermanyDepartment of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of KoreaAntibodies play a crucial role in our immune system. Their ability to bind to and neutralize pathogens opens opportunities to develop antibodies for therapeutic and diagnostic use. Computational methods capable of designing antibodies for a target antigen can revolutionize drug discovery, reducing the time and cost required for drug development. Artificial intelligence (AI) methods have recently achieved remarkable advancements in the design of protein sequences and structures, including the ability to generate scaffolds for a given motif and binders for a specific target. These generative methods have been applied to antigen-conditioned antibody design, with experimental binding confirmed for de novo-designed antibodies. This review surveys current AI methods used in antibody development, focusing on those for antigen-conditioned antibody design. The results obtained by AI-based methodologies in antibody and protein research suggest a promising direction for generating de novo binders for various target antigens.https://www.tandfonline.com/doi/10.1080/19420862.2025.2528902Antibody designgenerative artificial intelligencemachine learningprotein designstructural biology |
| spellingShingle | Luiz Felipe Vecchietti Bryan Nathanael Wijaya Azamat Armanuly Begench Hangeldiyev Hyunkyu Jung Sooyeon Lee Meeyoung Cha Ho Min Kim Artificial intelligence-driven computational methods for antibody design and optimization mAbs Antibody design generative artificial intelligence machine learning protein design structural biology |
| title | Artificial intelligence-driven computational methods for antibody design and optimization |
| title_full | Artificial intelligence-driven computational methods for antibody design and optimization |
| title_fullStr | Artificial intelligence-driven computational methods for antibody design and optimization |
| title_full_unstemmed | Artificial intelligence-driven computational methods for antibody design and optimization |
| title_short | Artificial intelligence-driven computational methods for antibody design and optimization |
| title_sort | artificial intelligence driven computational methods for antibody design and optimization |
| topic | Antibody design generative artificial intelligence machine learning protein design structural biology |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2025.2528902 |
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