Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Area of Science:

• Biotechnology
• Immunology
• Computational Biology

Background:

• Therapeutic antibodies represent a major class of biotherapeutics, crucial for treating various human diseases.
• The traditional design and discovery of antibody drugs are complex, lengthy, and resource-intensive processes.
• Artificial intelligence (AI) has emerged as a transformative technology in the life sciences.

Purpose of the Study:

• To review the application of machine learning (ML) methods in computational antibody design and discovery.
• To summarize ML-based approaches for predicting antibody structure, antigen interactions, and developability.
• To discuss the current status of ML-driven therapeutic antibodies in preclinical and clinical development.

Main Methods:

• Review of major machine learning (ML) algorithms and computational tools.
• Analysis of ML applications in predicting antibody structure and antigen-binding interfaces.
• Evaluation of ML models for assessing antibody developability characteristics.

Main Results:

• ML methods have demonstrated significant progress in antibody discovery, optimization, and developability prediction.
• Computational predictors utilizing ML are increasingly effective for antibody structure and antigen interface analysis.
• A growing number of ML-based therapeutic antibodies are progressing through preclinical and clinical trials.

Conclusions:

• Machine learning (ML) offers powerful tools to accelerate and enhance the design and discovery of therapeutic antibodies.
• ML-driven approaches are crucial for overcoming the challenges associated with traditional antibody drug development.
• Fully computational antibody design using ML holds promise for future therapeutic innovations.