# Constrained Submodular Optimization for Vaccine Design

arXiv: https://arxiv.org/abs/2206.08336 · HTML: https://mlsanity.com/paper/2206.08336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Maximum n-times Coverage for Vaccine Design](https://mlsanity.com/paper/2101.10902/) (2021)

## Abstract (arXiv)

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.