Deep learning in preclinical antibody drug discovery and development

Antibody drugs have become a key part of biotherapeutics. Patients suffering from various diseases have benefited from antibody therapies. However, its development process is rather long, expensive and risky. To speed up the process, reduce cost and improve success rate, artificial intelligence, especially deep learning methods, have been widely used in all aspects of preclinical antibody drug development, from library generation to hit identification, developability screening, lead selection and optimization. In this review, we systematically summarize antibody encodings, deep learning architectures and models used in preclinical antibody drug discovery and development. We also critically discuss challenges and opportunities, problems and possible solutions, current applications and future directions of deep learning in antibody drug development.

Automated summary

Antibody drugs have become essential in biotherapeutics and have benefited patients with various diseases. However, their development process is time-consuming, costly, and risky. To accelerate development, reduce costs, and increase success rates, artificial intelligence, particularly deep learning methods, are extensively used in all stages of preclinical antibody drug development. This review systematically summarizes the use of deep learning in antibody drug discovery and development, including antibody encodings, deep learning architectures, and models. We also critically discuss the challenges, opportunities, current applications, and future directions of deep learning in antibody drug development.