Potentially Highly Effective Drugs for COVID-19: Virtual Screening and Molecular Docking Study Through PyRx-Vina Approach
Bibliographic record
Abstract
Introduction: The World Health Organization (WHO) has declared the novel coronavirus (COVID-2019) infection outbreak a global health emergency. Drug repurposing, which concerns the investigation of existing drugs for new therapeutic target indications, has emerged as a successful strategy for drug discovery due to the reduced costs and expedited approval procedures.Material and Methods: The crystal structure of a protein essential for virus replication has been filed in the Protein Data Bank recently. Based on this structure and existing experimental datasets for SARS-CoV2(COVID-19) we present results deriving from the virtual screening of a database of more than 1000 drugs in the DrugBank that have been approved by Food and Drug Administration (FDA).Results: Results showed that some of the known protease inhibitors currently used in HIV and Cancer infections might be helpful for the therapy of COVID-19 also. Results also showed that Levomefolic acid, or vitamin B9, is recommended therapy because of its oral sources and no side effects.Conclusion: Between all studied FDA-approved drug, VitaminB9 and Etoposide which used for HIV protease inhibitor, revealed strong interaction with protease binding pocket and placed well into the pocket even better than the lopinavir-ritonavir, and since this compound is FDA-approved and successfully passed various testing steps, therefor there is a hope that this drug, could be a potential drug to treating the COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".