System Theoretic Methods in Drug Discovery and Vaccine Formulation: Review and Perspectives

The methods utilized in the drug discovery pipeline routinely combine machine learning and deep learning algorithms to enhance the outputs. The generation of a drug target, through virtual screening and computational analysis of databases used for target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. Recent technological advances in human immunology have provided improved tools that allow a better understanding of the biological and molecular mechanisms leading to the protective human immune response to pathogens, inspiring new strategies for vaccine design. Immunoinformatics approaches are more beneficial, and thus there is a demand for modern technologies such as reverse vaccinology, structural vaccinology, and system approaches in developing potential vaccine candidates. System theory, defined as a set of machine learning, control theory, and optimization-based methods applied to networked systems, provides a unifying framework for modeling and analyzing biological complexity. In this review, we explore the application of such computational methods at every stage of the therapeutic pipeline, including lead discovery, optimization, and dosing, as well as vaccine target prediction and immunogen design. Here, we summarize the system theoretic methods which provide insights into developed approaches and their applications in rational drug discovery and vaccine formulations. The approaches ranged in the review yield accurate predictions and insights. This review is intended to serve as a resource for researchers seeking to understand, adopt, or build upon system theoretic techniques in drug and vaccine development, offering both conceptual foundations and practical directions.