Computational Repurposing of Relatively Large Drugs for the Receptor Binding Domain of SARS‐CoV‐2 Spike Protein
Bibliographic record
Abstract
Abstract Currently, therapeutic options for COVID‐19 remain limited. While drug repurposing offers a rapid strategy to address this gap, most studies focus on small molecules, overlooking larger drugs (>500 Da) that may exhibit optimized pharmacokinetics and translational potential. Here, we computationally repurpose FDA‐approved large drugs (MW > 500, LogP ≤ 5) targeting the receptor‐binding domain (RBD) of the SARS‐CoV‐2 spike protein. Through molecular docking, molecular dynamics (MD) simulations (100 ns), and binding energy calculations, we prioritized candidates with high affinity for the RBD. Notably, Atazanavir, Valrubicin, Telotristat, and Clotrimazole (identified here) interact with hotspot residues critical for ACE2 binding, suggesting a mechanism to disrupt viral entry. While our findings align with experimental mutagenesis data, they also highlight underexplored opportunities in large‐drug repurposing. This work provides a focused candidate list for experimental validation, complementing existing literature by broadening the scope of actionable targets.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".