How to think about designing smart antibodies in the age of genAI: integrating biology, technology, and experience

• Published in: mAbs Vol. 17; no. 1; p. 2490790
• Main Authors: Buchanan, Andrew, Bennett, Eric, Croasdale-Wood, Rebecca, Evers, Andreas, Fennell, Brian, Furtmann, Norbert, Krawczyk, Konrad, Kumar, Sandeep, Langmead, Christopher James, Shahsavarian, Melody, Tinberg, Christine Elaine
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 01.12.2025; Taylor & Francis Group
• Subjects: Animals; Antibodies, Monoclonal - chemistry; Antibodies, Monoclonal - therapeutic use; Antibody; Artificial Intelligence; candidate drug; Comment; Drug Design; Drug Discovery - methods; Humans; Machine Learning; machine learning; Antibody; candidate drug; artificial intelligence
• ISSN: 1942-0862; 1942-0870; 1942-0870
• DOI: 10.1080/19420862.2025.2490790

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.