ZINC003873365 for COVID-19

c19early.org

COVID-19 Treatment Clinical Evidence

COVID-19 involves the interplay of 400+ viral and host proteins and factors, providing many therapeutic targets. c19early analyzes 6,000+ studies for 210+ treatments—over 17 million hours of research. Only three high-profit early treatments are approved in the US. In reality, many treatments reduce risk, with 25 low-cost treatments approved across 163 countries.

Naso/oropharyngeal treatment Effective Treatment directly to the primary source of initial infection.
Healthy lifestyles Protective Exercise, sunlight, a healthy diet, and good sleep all reduce risk.
Immune support Effective Vitamins A, C, D, and zinc show reduced risk, as with other viruses.
Thermotherapy Effective Methods for increasing internal body temperature, enhancing immune system function.
Systemic agents Effective Many systemic agents reduce risk, and may be required when infection progresses.
High-profit systemic agents Conditional Effective, but with greater access and cost barriers.
Monoclonal antibodies Limited Utility Effective but rarely used—high cost, variant dependence, IV/SC admin.
Acetaminophen Harmful Increased risk of severe outcomes and mortality.
Remdesivir Harmful Increased mortality with longer followup. Increased kidney and liver injury, cardiac disorders.

ZINC003873365 may be beneficial for COVID-19 according to the studies below. COVID-19 involves the interplay of 400+ viral and host proteins and factors providing many therapeutic targets. Scientists have proposed 11,000+ potential treatments. c19early.org analyzes 210+ treatments. We have not reviewed ZINC003873365 in detail.

Studies suggesting benefit with ZINC003873365

Aqeel et al., Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches, Scientific Reports, doi:10.1038/s41598-025-02773-7

Aqeel, I., To Explore the Potential Inhibitors against Multitarget Proteins of COVID 19 using In Silico Study, arXiv, doi:10.48550/arXiv.2409.16486

The global pandemic due to emergence of COVID 19 has created the unrivaled public health crisis. It has huge morbidity rate never comprehended in the recent decades. Researchers have made many efforts to find the optimal solution of this pandemic. Progressively, drug repurposing is an emergent and powerful strategy with saving cost, time, and labor. Lacking of identified repurposed drug candidates against COVID 19 demands more efforts to explore the potential inhibitors for effective cure. In this study, we used the combination of molecular docking and machine learning regression approaches to explore the potential inhibitors for the treatment of COVID 19. We calculated the binding affinities of these drugs to multitarget proteins using molecular docking process. We perform the QSAR modeling by employing various machine learning regression approaches to identify the potential inhibitors against COVID 19. Our findings with best scores of R2 and RMSE demonstrated that our proposed Decision Tree Regression (DTR) model is the most appropriate model to explore the potential inhibitors. We proposed five novel promising inhibitors with their respective Zinc IDs ZINC (3873365, 85432544, 8214470, 85536956, and 261494640) within the range of -19.7 kcal/mol to -12.6 kcal/mol. We further analyzed the physiochemical and pharmacokinetic properties of these most potent inhibitors to examine their behavior. The analysis of these properties is the key factor to promote an effective cure for public health. Our work constructs an efficient structure with which to probe the potential inhibitors against COVID-19, creating the combination of molecular docking with machine learning regression approaches.