A Contrastive Learning Framework for Efficient Viral Escape Prediction

Understanding the complex rules and mechanisms behind viral evolution is crucial for developing better preventive treatments, yet predicting immune-evading mutations remains challenging. Recent advances in protein language models have led to novel approaches for in silico analysis of viral escape. We introduce CoV-SNN, unifying variant classification and escape prediction within an efficient contrastive learning framework. CoV-SNN, built on a Siamese neural network architecture, classifies previously unseen variants by modeling sequence-level similarities and differences through embeddings from CoV-RoBERTa, a lightweight protein language model trained on high-quality SARS-CoV-2 Spike sequences. It prioritizes escape sequences using an enhanced Constrained Semantic Change Search (CSCS) function that maps antigenic variation to semantic change and viral fitness to sequence probability. We evaluate CoV-SNN on novel sequences containing both wet-lab-verified and computationally generated escape mutations. CoV-SNN achieves 98.8% accuracy in multi-class variant classification, an AUC of 0.909 in zero-shot variant classification, and 97.7% escape precision at Top-10%, while providing up to a 125-fold inference speedup. These results suggest that contrastive learning can support scalable in silico surveillance and prioritization of immune-evasive mutations.