Artificial intelligence advancements in monoclonal antibody development technology.

Area of Science:

• Biotechnology and Pharmaceutical Sciences
• Computational Biology and Bioinformatics

Background:

• Monoclonal antibodies are crucial therapeutics for various diseases.
• Traditional antibody development is time-consuming and labor-intensive.
• There is a growing need for efficient and cost-effective antibody engineering strategies.

Purpose of the Study:

• To review advancements in AI-driven in silico tools for antibody discovery and optimization.
• To provide a comprehensible overview of AI applications in antibody engineering.
• To highlight the integration of computational methods with experimental validation.

Main Methods:

• Systematic literature analysis of clinical and research databases.
• Summarization of AI applications in antibody sequence design, epitope-paratope prediction, and affinity optimization.
• Assessment of AI's role in structural prediction and developability.

Main Results:

• AI models significantly enhance antibody sequence design and affinity optimization.
• AI tools improve the accuracy of epitope-paratope predictions.
• AI aids in predicting antibody structure and assessability for development.

Conclusions:

• The integration of AI-driven computational tools is essential for next-generation antibody development.
• Combining AI with experimental validation offers a powerful and complementary approach.
• This integrated strategy leads to accurate, efficient, and clinically relevant antibody therapeutics.