  Advances in machine learning have enabled the prediction of immune system
responses to prophylactic and therapeutic vaccines. However, the engineering
task of designing vaccines remains a challenge. In particular, the genetic
variability of the human immune system makes it difficult to design peptide
vaccines that provide widespread immunity in vaccinated populations. We
introduce a framework for evaluating and designing peptide vaccines that uses
probabilistic machine learning models, and demonstrate its ability to produce
designs for a SARS-CoV-2 vaccine that outperform previous designs. We provide a
theoretical analysis of the approximability, scalability, and complexity of our
framework.
