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.
Cite
@article{arxiv.2206.08336,
title = {Constrained Submodular Optimization for Vaccine Design},
author = {Zheng Dai and David Gifford},
journal= {arXiv preprint arXiv:2206.08336},
year = {2023}
}