Discovery of TRPV4-Targeting Small Molecules with Anti-Influenza Effects Through Machine Learning and Experimental Validation

作者：Yan Sun, Jiajing Wu, Beilei Shen, Hengzheng Yang, Huizi Cui, Weiwei Han, Rongbo Luo, Shijun Zhang, He Li, Bingshuo Qian, Lingjun Fan, Junkui Zhang, Tiecheng Wang, Xianzhu Xia, Fang Yan, Yuwei Gao · 发表于：International Journal of Molecular Sciences · 年份：2025 · DOI：10.3390/ijms26031381 · 被引用次数：3 · 研究领域：Ion Channels and Receptors、Influenza Virus Research Studies、Herbal Medicine Research Studies

homeostasis and is essential in regulating immune responses, metabolic processes, and signal transduction. Recent studies have shown that TRPV4 activation enhances influenza A virus infection, promoting viral replication and transmission. However, there has been limited exploration of antiviral drugs targeting the TRPV4 channel. In this study, we developed the first machine learning model specifically designed to predict TRPV4 inhibitory small molecules, providing a novel approach for rapidly identifying repurposed drugs with potential antiviral effects. Our approach integrated machine learning, virtual screening, data analysis, and experimental validation to efficiently screen and evaluate candidate molecules. For high-throughput virtual screening, we employed computational methods to screen open-source molecular databases targeting the TRPV4 receptor protein. The virtual screening results were ranked based on predicted scores from our optimized model and binding energy, allowing us to prioritize potential inhibitors. Fifteen small-molecule drugs were selected for further in vitro and in vivo antiviral testing against influenza. Notably, glecaprevir and everolimus demonstrated significant inhibitory effects on the influenza virus, markedly improving survival rates in influenza-infected mice (protection rates of 80% and 100%, respectively). We also validated the mechanisms by which these drugs interact with the TRPV4 channel. In summary, our study presents the first predictiv...