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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".