Abstract: Influenza viruses undergo a series of genetic mutations under selective pressure,leading to antigenic variation,immune evasion,and enhanced adaptability,which reduces the effectiveness of existing vaccines and antiviral drugs. Timely identification of antigenic differences between viral strains is crucial to the prevention and control of influenza viruses and the development of vaccines. Due to the low throughput of traditional serological methods,the available data samples are often limited,making it difficult for existing deep learning-based antigenicity prediction models to effectively extract antigenic features from hemagglutinin protein sequences. Therefore,this paper proposes an antigenicity prediction method enhanced by convolutional neural networks and contrastive learning. By comparing the genetic sequences and antigenicity labels of original strains,the model directly extracts antigenic representation differences and visualizes these antigenic differences. Experiments are conducted on datasets of three subtypes:A/H1N1,A/H3N2,and A/H5N1. The results show that the proposed model improves the accuracy and generalization ability of antigenicity prediction,providing support for the monitoring of influenza viruses and vaccine development.

Key words: Influenza A, Antigenicity prediction, Contrastive learning, Convolutional attention