Artificial intelligence advancements in monoclonal antibody development technology

Monoclonal antibody-based therapeutics have become essential tools for treating infectious, autoimmune, and malignant diseases due to their high specificity and efficacy. As their clinical and scientific relevance continues to expand, the need for faster, more accurate and cost-effective development strategies has grown. Traditional laboratory-based methods for antibody design and improving remain reliable but are time-consuming, labor-intensive, and limited by experimental constraints. These challenges have driven a shift toward the integration of computational methods as a complementary approach for antibody engineering. The current review provides a simplified overall explanation of recent advancements in artificial intelligence (AI)-driven in silico tools used to accelerate and enhance the process of antibody discovery and optimization. We have systematically analyzed literature from clinical and research databases and summarized obtained data into a comprehensible overview. We highlighted how AI models contribute to sequence design, epitope-paratope predictions, affinity optimization, structural prediction and developability assessment. In conclusion, the most effective strategy for next-generation monoclonal antibody development relies on the integration of computational prediction and design tools followed by experimental validation. Combining AI-driven innovation with traditional laboratory methods represents a powerful and complementary approach for achieving accurate, efficient, and clinically relevant antibody therapeutics.