Constrained Submodular Optimization for Vaccine Design

• URL: http://arxiv.org/abs/2206.08336v1
• Date: Thu, 16 Jun 2022 17:40:54 GMT
• Title: Constrained Submodular Optimization for Vaccine Design
• Authors: Zheng Dai, David Gifford
• Abstract summary: Genetic variability 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. We demonstrate its ability to produce designs for a SARS-CoV-2 vaccine that outperform previous designs.
• Score: 1.7622426179653559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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