Clenbuterol for COVID-19

RNA viruses, such as SARS-CoV-2 and influenza, pose a persistent threat to global public health. Their high mutation rates undermine the effectiveness of conventional direct-acting antivirals (DAAs) and facilitate drug resistance. As obligate intracellular parasites, RNA viruses rely extensively on host cellular machinery and metabolic pathways throughout their life cycle. This dependency has prompted a strategic shift in antiviral research—from targeting the mutable virus to targeting relatively conserved host dependency factors (HDFs). In this review, we systematically analyze how RNA viruses exploit HDFs at each stage of infection: utilizing host receptors for entry; remodeling endomembrane systems to establish replication organelles; hijacking transcriptional, translational, and metabolic systems for genome replication and protein synthesis; and co-opting trafficking and budding machinery for assembly and egress. By comparing strategies across diverse RNA viruses, we highlight the broad-spectrum potential of HDF-targeting approaches, which offer a higher genetic barrier to resistance, providing a rational framework for developing host-targeting antiviral therapies.

The COVID-19 pandemic has demanded urgent and accelerated action toward developing effective therapeutic strategies. Drug repurposing models (in silico) are in high demand and require accurate and reliable molecular interaction data. While experimentally verified viral–host interaction data (SARS-CoV-2–human interactions published on April 30, 2020) provide an invaluable resource, these datasets include only a limited number of high-confidence interactions. Here, we extend these resources using a deep learning–based multiview graph neural network approach, coupled with optimal transport–based integration. Our comprehensive validation strategy confirms 472 high-confidence predicted interactions between 280 host proteins and 27 SARS-CoV-2 proteins. The proposed model demonstrates robust predictive performance, achieving ROC-AUC scores of 85.9% (PPI network), 83.5% (GO similarity network), and 83.1% (sequence similarity network), with corresponding average precision scores of 86.4%, 82.8%, and 82.3% on independent test sets. Comparative evaluation shows that our multiview approach consistently outperforms conventional single-view and baseline graph learning methods. The model combines features derived from protein sequences, gene ontology terms, and physical interaction information to improve interaction prediction. Furthermore, we systematically map the predicted host factors to FDA-approved drugs and identify several candidates, including lenalidomide and pirfenidone, which have established or emerging roles in COVID-19 therapy. Overall, our framework provides comprehensive and accurate predictions of SARS-CoV-2–host protein interactions and represents a valuable resource for drug repurposing efforts.