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

Authors

Junhan Dong, Zhiyun Cheng, Yong Diao

Published in

Viruses. Volume 18. Issue 10. Oct 03, 2026. Epub Oct 03, 2026.

Abstract

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.

PMID:
42944013
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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