Combining computational and experimental evidence on the activity of antimalarial drugs on <scp>papain‐like</scp> protease of <scp>SARS‐CoV</scp>‐2: A repurposing study
Bibliographic record
Abstract
The development of inhibitors that target the papain-like protease (PLpro) has the potential to counteract the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent causing coronavirus disease 2019 (COVID-19). Based on a consideration of its several downstream effects, interfering with PLpro would both revert immune suppression exerted by the virus and inhibit viral replication. By following a repurposing strategy, the current study evaluates the potential of antimalarial drugs as PLpro inhibitors, and thereby the possibility of their use for treatment of SARS-CoV-2 infection. Computational tools were employed for structural analysis, molecular docking, and molecular dynamics simulations to screen antimalarial drugs against PLpro, and in silico data were validated by in vitro experiments. Virtual screening highlighted amodiaquine and methylene blue as the best candidates, and these findings were complemented by the in vitro results that indicated amodiaquine as a μM PLpro deubiquitinase inhibitor. The results of this study demonstrate that the computational workflow adopted here can correctly identify active compounds. Thus, the highlighted antimalarial drugs represent a starting point for the development of new PLpro inhibitors through structural optimization.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".