How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience
Antibody discovery has been successful in designing and progressing molecules to the clinic and market based on largely empirical methods and human experience. The field is now transitioning from classical monospecific antibodies to innovative smart biologics that employ diverse mechanisms of action...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | mAbs |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2025.2490790 |
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| _version_ | 1850212047983214592 |
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| author | Andrew Buchanan Eric Bennett Rebecca Croasdale-Wood Andreas Evers Brian Fennell Norbert Furtmann Konrad Krawczyk Sandeep Kumar Christopher James Langmead Melody Shahsavarian Christine Elaine Tinberg |
| author_facet | Andrew Buchanan Eric Bennett Rebecca Croasdale-Wood Andreas Evers Brian Fennell Norbert Furtmann Konrad Krawczyk Sandeep Kumar Christopher James Langmead Melody Shahsavarian Christine Elaine Tinberg |
| author_sort | Andrew Buchanan |
| collection | DOAJ |
| description | Antibody discovery has been successful in designing and progressing molecules to the clinic and market based on largely empirical methods and human experience. The field is now transitioning from classical monospecific antibodies to innovative smart biologics that employ diverse mechanisms of action, such as targeting, antagonism, agonism, and target-independent function. This evolution is being assisted, augmented, and potentially disrupted by artificial intelligence and machine learning (AI/ML) technologies. This perspective is focused on bringing clarity to the strategy and thinking that is required when designing antibody drug candidates and how emerging AI/ML strategies can address the real-world challenges of drug discovery and continue to improve performance. |
| format | Article |
| id | doaj-art-3dba4def45ba41dd93879e50d6d00e24 |
| institution | OA Journals |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-3dba4def45ba41dd93879e50d6d00e242025-08-20T02:09:25ZengTaylor & Francis GroupmAbs1942-08621942-08702025-12-0117110.1080/19420862.2025.2490790How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experienceAndrew Buchanan0Eric Bennett1Rebecca Croasdale-Wood2Andreas Evers3Brian Fennell4Norbert Furtmann5Konrad Krawczyk6Sandeep Kumar7Christopher James Langmead8Melody Shahsavarian9Christine Elaine Tinberg10Biologics Engineering, AstraZeneca R&D, Cambridge, UKBioMedicine Design, Pfizer Research & Development, Cambridge, MA, USABiologics Engineering, AstraZeneca R&D, Cambridge, UKAntibody Discovery & Protein Engineering, Merck Healthcare KGaA, Darmstadt, GermanyBiomedicine Design, Pfizer Research & Development, Dublin, IrelandR&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Frankfurt Am Main, GermanyNaturalAntibody, Szczecin, PolandMolecule Design and Modelling, Moderna Inc., Cambridge, MA, USACenter for Research Acceleration by Digital Innovation, Amgen, Thousand Oaks, CA, USABiotherapeutics Discovery Research, Eli Lilly & Company, San Diego, CA, USALarge Molecule Discovery & Research Data Science, Amgen, South San Francisco, CA, USAAntibody discovery has been successful in designing and progressing molecules to the clinic and market based on largely empirical methods and human experience. The field is now transitioning from classical monospecific antibodies to innovative smart biologics that employ diverse mechanisms of action, such as targeting, antagonism, agonism, and target-independent function. This evolution is being assisted, augmented, and potentially disrupted by artificial intelligence and machine learning (AI/ML) technologies. This perspective is focused on bringing clarity to the strategy and thinking that is required when designing antibody drug candidates and how emerging AI/ML strategies can address the real-world challenges of drug discovery and continue to improve performance.https://www.tandfonline.com/doi/10.1080/19420862.2025.2490790Antibodyartificial intelligencemachine learningcandidate drug |
| spellingShingle | Andrew Buchanan Eric Bennett Rebecca Croasdale-Wood Andreas Evers Brian Fennell Norbert Furtmann Konrad Krawczyk Sandeep Kumar Christopher James Langmead Melody Shahsavarian Christine Elaine Tinberg How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience mAbs Antibody artificial intelligence machine learning candidate drug |
| title | How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience |
| title_full | How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience |
| title_fullStr | How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience |
| title_full_unstemmed | How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience |
| title_short | How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience |
| title_sort | how to think about designing smart antibodies in the age of genai integrating biology technology and experience |
| topic | Antibody artificial intelligence machine learning candidate drug |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2025.2490790 |
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