Constrained Submodular Optimization for Vaccine Design
This addresses the problem of vaccine design for populations with genetic variability, but appears incremental as it builds on existing methods.
The authors tackled the challenge of designing peptide vaccines for widespread immunity by introducing a framework using probabilistic machine learning models, and demonstrated it outperforms previous designs for a SARS-CoV-2 vaccine.
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.