Repurposing of Relatively Large Drugs for the Receptor Binding Domain of SARS-CoV-2 Spike Protein
Bibliographic record
Abstract
Nowadays, there are a few therapeutics to prevent or treat COVID-19. Because the development of safe and effective drugs is expensive and time-consuming, drug repurposing is now part of the arsenal of drug discovery. Herein, we focus on the repurposing of relatively large FDA-approved drugs (MW > 500, LogP ≤ 5) that target the receptor-binding domain (RBD) of SARS-CoV-2 spike protein. We performed a computational study incorporating molecular docking, molecular dynamics simulations, and relative binding energy calculations to discover prospective compounds with high affinity towards the viral RBD. We found that the most promising drugs, namely, Atazanavir, Zazole, Valrubicin, and Telotristat, influence hotspot residues of the RBD protein and may interfere with the human angiotensin-converting enzyme-2 (ACE2) receptor. Our findings corroborate with the present literature and can accelerate the rational design of selective inhibitors against SARS-CoV-2.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".