Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates. In this chapter, we describe a deep learning-based technique which utilizes 18 biological and 9154 physicochemical properties of proteins for finding potential vaccine candidates. Using this technique, a new web-based system, named Vaxi-DL, was developed which helped in finding new vaccine candidates from bacteria, protozoa, viruses, and fungi. Vaxi-DL is available at: https://vac.kamalrawal.in/vaxidl/ .
Keywords: Antigen prediction; Artificial intelligence; COVID-19; Deep learning; Deep learning pathogen models; In silico vaccine development; Machine learning; Vaccine; Vaccine design; Vaxi-DL; mRNA vaccines.
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.