Molecular docking-simulation edge assessment of potential and less-toxic ‘anti- HIV-drug and phyto-flavonoid’ combination against COVID-19
Bibliographic record
Abstract
Abstract The emergence of the pandemic coronavirus-2019 (COVID-19) disease by the Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) or 2019-novel coronavirus-2019 (2019- nCoV-2019) has created a disease-ridden environment for the entire human community, globally. However, no potent prophylactic therapy is available to control the deadly emerged viral disease. Repurposing of existing antiviral, antiinflammatory, antimalarial drugs is the only option against SARS-CoV-2. But without any clinical evidence, the recommended dose and expected side effects are under debate. As an alternative solution, we proposed a newer hypothesis using the selective, potent anti-HIV drugs and flavonoid class of phytochemicals in combination to balance the potency and toxicity during combat against SARS-CoV-2. Primarily, ten anti-HIV protease inhibitor drugs with ten phyto-flavonoids are selected as ligands for docking study against the putative target, the main protease (M pro ) of SARS-CoV-2 (PDB ID: 6Y2E), as an essential enzyme in viral genome replication. According to molecular docking and drug-ability scores of each ligand, the anti-HIV drug, the darunavir (with a docking score, -10.25 kcal/mol and drug-likeness rating, 0.60) and the quercetin-3-rhamnoside (with a docking score, -10.90 kcal/mol and drug-likeness rating, 0.82), were selected for further analysis in the mixture. Later, the interchanged mutual docking analysis suggested that ‘darunavir-quercetin-3- rhamnoside’ was the most potent and less toxic drug chemical-cocktail/ formulation against SARS-CoV-2-M pro . Additionally, molecular dynamics simulation, predicted toxicity and pharmacokinetics profiles also support to the hypothesized formulation; mainly, eight strong H- bond interactions were found against SARS-CoV-2-M pro . Thus, projected molecular docking- simulation based active and lesser toxic ‘anti-HIV-drug-phyto-flavonoid’ therapy could be promoted against SARS-CoV-2.
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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 | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| 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".