In silico investigations on the repurposing of antivirals for Covid-19 and pharmacophore modelling
Bibliographic record
Abstract
The pandemic COVID-19 has been spreading around the globe from December 2019 onwards and is considered the most infectious disease of this century. To date, there is no effective drug against SARS-CoV-2 discovered by pharmaceutical scientists, and the research is going rigorously all over the world. In this work, we examined the interaction of the already existing antivirals (Lopinavir, Atazanavir, and Remdesivir) with the structural proteins of SARS-CoV-2 using computational methods. Pharmacophore modeling of these drugs was conducted using molecular databases to determine the lead compounds from molecular databases. Pharmagist Webserver and Zinc Molecular Database were used to find out the pharmacophore and lead compounds, respectively. The drug-likeness properties of the compounds were evaluated by the SwissADME webserver. In silico studies showed that the binding affinities of the drugs followed the order Remdesivir > Atazanavir > Lopinavir. Docking and pharmacological studies revealed the potency and drug-likeness of the synthetic molecules.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".