In-silico Identification of Dexamethasone-similar Compounds AgainstSARS-Cov-2 Spike Protein: A Drug Repurposing Approach
Bibliographic record
Abstract
Abstract: To identify potential drug candidates for the treatment of COVID-19 using a computational method. The recent pandemic of COVID-19 is observed as not less than a natural calamity of humankind and raised serious concerns for its immediate management. The continued spread of coronavirus disease across the globe poses a significant threat to human health. Out of this, the application of Dexamethasone has been correlated with reduced mortality in COVID-19 cases. This study sheds new light on the pharmacological potential of Dexamethasone and similar compounds in mitigating SARSCoV2 infection. : In this study, we explored Dexamethasone-similar compounds, which can modulate the binding of SARS-CoV-2 spike protein to the host and TH17 programming in the host using a computer-aided drug repurposing method. The docking studies indicate that Desoximetasone can bind to the spike proteins of SARS-CoV-2, which are crucial for viral attachment and entry into host cells. By competing with these spike proteins, Desoximetasone may interfere with the virus's ability to attach to and enter host cells, potentially inhibiting viral replication and spread. The results from molecular dynamic simulation analysis further support this notion by demonstrating that Desoximetasone has a strong interaction with the binding sites of the spike protein. Experimental validation through in vitro studies and clinical trials is needed to evaluate its potential as a treatment option for COVID-19. Together, these findings revealed the underlying mechanism of how Desoximetasone can influence the fate of the virus in the host and advocated its anti-viral potential.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".