Searching for potential drugs against SARS-CoV-2 through virtual screening on several molecular targets
Bibliographic record
Abstract
The acute respiratory syndrome caused by the SARS-CoV-2, known as COVID-19, has been ruthlessly tormenting the world population for more than six months. However, so far no effective drug or vaccine against this plague have emerged yet, despite the huge effort in course by researchers and pharmaceutical companies worldwide. Willing to contribute with this fight to defeat COVID-19, we performed a virtual screening study on a library containing Food and Drug Administration (FDA) approved drugs, in a search for molecules capable of hitting three main molecular targets of SARS-CoV-2 currently available in the Protein Data Bank (PDB). Our results were refined with further molecular dynamics (MD) simulations and MM-PBSA calculations and pointed to 7 multi-target hits which we propose here for experimental evaluation and repurposing as potential drugs against COVID-19. Additional rounds of docking, MD simulations and MM-PBSA calculations with remdesivir suggested that this compound can also work as a multi-target drug against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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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 | 0.000 | 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".