Computational Vaccine Design Strategies

Abstract

Computational vaccine design leverages immunoinformatics, structural modelling, and machine learning to identify antigenic targets, predict immune epitopes, and optimise vaccine constructs in silico before experimental validation, accelerating the development timeline from years to months as demonstrated during the COVID-19 pandemic, yet adoption of computational approaches varies widely across design strategy categories. We evaluated 214 computational vaccine design programmes across centres in France and Switzerland between 2017 and 2021, spanning five strategy categories: T-cell epitope prediction and MHC binding, B-cell epitope and antigen surface mapping, reverse vaccinology from pathogen genomes, structure-guided immunogen engineering, and mRNA and delivery vector optimisation. A Computational Vaccine Design Index (CVDI) was constructed from five sub-scores -- epitope prediction accuracy, immunogenicity validation rate, population coverage breadth, structural fidelity of designed constructs, and pipeline-to-candidate speed -- with weights from regression against sustained adoption into vaccine development pipelines. CVDI correlated with adoption at r = +0.84 and discriminated adopted from non-adopted methods with an AUC of 0.882. T-cell epitope prediction scored highest (mean CVDI 0.824), while mRNA optimisation trailed at 0.600. Only 35.5 percent exceeded the 0.75 threshold. Epitope prediction accuracy carried the largest weight (beta = +0.280), followed by immunogenicity validation (beta = +0.228).