AAV-CapNet: Multi-Omics Deep Learning with Fourier Enhancement and Cross-Attention for Predicting Infectivity of Known AAV Capsid Variants

This work builds AAV-CapNet, a deep learning model integrating Fourier frequency enhancement and cross-attention fusion, to predict the infectivity of 20 AAV capsid variants across 52 cancer cell lines with a self-built multi-omics dataset of 392 validated measurements. Validated via stratified splitting and repeated five-fold cross-validation, it extracts dual-domain capsid features fused with host transcriptomics for joint classification and regression. The model exhibits favourable performance under low-false-positive-rate conditions suitable for high-throughput screening. Attention mapping and ablation tests validate core modules' necessity. Despite poor generalization to unseen serotypes, this interpretable tool facilitates intraserotype capsid screening and rational engineering.