Predicting genetic evolution of viruses to identify suitable vaccines using artificial intelligence.

Area of Science:

• Virology
• Genomic Epidemiology
• Artificial Intelligence

Background:

• Viral evolution poses a global challenge to vaccine development due to immune escape and reduced efficacy of existing vaccines.
• Traditional genomic epidemiology methods are retrospective and cannot predict future viral evolutionary trends.

Purpose of the Study:

• To introduce a novel AI framework, the Refined Deep Evolutionary Learning Framework (R-DELF), for proactive prediction of viral mutations.
• To assess vaccine suitability against emerging viral variants using genomic, structural, and temporal data.

Main Methods:

• The R-DELF framework integrates an ESM-2 Transformer for structure-aware embeddings and dual-attention Graph Neural Networks (GNNs) for phylogenetic and structural dependency learning.
• An evolutionary learning maximiser enhances adaptation modelling, complemented by an Explainable AI layer for residue-level attribution and interpretability.
• The framework is implemented in Python using TensorFlow, leveraging genomic and protein data.

Main Results:

• R-DELF achieves high experimental accuracy (99.2%), precision (97.92%), recall (98.89%), and F1-score (99.4%), outperforming current AI-based virology models.
• The framework demonstrates the ability to predict high-risk mutations in advance.

Conclusions:

• R-DELF offers a significant advancement in predicting viral evolution and assessing vaccine suitability.
• The framework enhances pandemic preparedness by enabling timely vaccine production and data-driven predictions of viral evolutionary trajectories.