Erythromycin estolate for COVID-19

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.

Coronavirus, a large family of positive-sense RNA viruses, are responsible for both mild and severe respiratory illnesses, ranging from the common cold to life-threatening conditions. Despite significant advances in vaccine and antiviral development, the high mutability of human coronaviruses (HCoVs), such as SARS-CoV-2, presents a major challenge in treating these infections. Effective, broad-spectrum antiviral drugs are urgently needed to address both current and future HCoV outbreaks. Here, we conducted high-throughput screening of a natural product library containing 3407 compounds to identify potential antiviral agents against HCoV-OC43 and HCoV-229E. We identified several natural products with inhibitory effects on HCoV-229E, HCoV-OC43, and the SARS-CoV-2 variants Delta (B.1.617.2) and Omicron (BA.5) in vitro without evident cytotoxicity. Among these, dibenzoylmethane (DBM) not only demonstrated broad-spectrum anticoronavirus activity in vitro but also effectively inhibited HCoV-OC43 replication in a BALB/c mouse model. Pharmacokinetic analysis revealed that DBM, when administered orally, maintained effective concentrations in the blood over an extended period, suggesting its suitability for oral administration. Mechanistically, DBM was found to regulate caspase-6, a host factor that suppresses interferon signalling and promotes HCoV replication. These findings highlight DBM as a promising candidate for the development of therapeutics targeting HCoVs, offering potential for treating infections by both established and emerging HCoVs.