Potential anti-viral activity of approved repurposed drug against main protease of SARS-CoV-2: an <i>in silico</i> based approach
Bibliographic record
Abstract
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which was first reported in Wuhan province of China, has become a deadly pandemic causing alarmingly high morbidity and mortality. In the absence of new targeted drugs and vaccines against SARS-CoV-2 at present, the choices for effective treatments are limited. Therefore, considering the exigency of the situation, we focused on identifying the available approved drugs as potential inhibitor against the promising Coronavirus drug target, the Main Protease, using computer-aided methods. We created a library of U. S. Food and Drug Administration approved anti-microbial drugs and virtually screened it against the available crystal structures of Main Protease of the virus. The study revealed that Viomycin showed the highest -CDocker energy after docking at the active site of SARS-CoV-2 Main Protease. It is noteworthy that Viomycin showed higher -CDocker energy as compared to the drugs currently under clinical trial for SARS-CoV-2 treatment viz. Ritonavir and Lopinavir. Additionally, Viomycin formed higher number of H-bonds with SARS-CoV-2 Main Protease than its co-crystallised inhibitor compound N3. Molecular dynamics simulation further showed that Viomycin embedded deeply inside the binding pocket and formed robust binding with SARS-CoV-2 Main Protease. Therefore, we propose that Viomycin may act as a potential inhibitor of the Main Protease of SARS-CoV-2. Further optimisations with the drug may support the much-needed rapid response to mitigate the pandemic.Communicated by Ramaswamy H. Sarma
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".