July 19, 2025Open Access

Artificial intelligence-driven computational methods for antibody design and optimization

LVLuiz Felipe VecchiettiMax Planck Institute for Security and Privacy BWBryan Nathanael WijayaKorea Advanced Institute of Science and Technology AAAzamat ArmanulyKorea Advanced Institute of Science and Technology

Key Points

AI-driven methods enhance antibody design, fostering innovative approaches in therapeutic and diagnostic applications.
Computational techniques allow quicker and cost-effective development of antibodies by optimizing protein sequences.
Experimental validation of AI-designed antibodies demonstrates their potential as effective binders against target antigens.
The review emphasizes advancements in generative methods for designing de novo antibodies, pointing to new possibilities in research.

Abstract

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.

AI से पूछें

Bookmark

View Full Paper