In Silico Identification of Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro)
Bibliographic record
Abstract
The ongoing Coronavirus Disease 19 (COVID-19) pandemic has had a profound impact on the global healthcare system. As the SARS-CoV-2 virus, responsible for this pandemic, continues to spread and develop mutations in its genetic material, new variants of interest (VOIs) and variants of concern (VOCs) are emerging. These outbreaks lead to a decrease in the efficacy of existing treatments such as vaccines or drugs, highlighting the urgency of new therapies for COVID-19. Therefore, in this study, we aimed to identify potential SARS-CoV-2 antivirals using a virtual screening protocol and molecular dynamics simulations. These techniques allowed us to predict the binding affinity of a database of compounds with the virus Mpro protein. This in silico approach enabled us to identify twenty-two chemical structures from a public database (QSAR Toolbox Ver 4.5 ) and ten promising molecules from our in-house database. The latter molecules possess advantageous qualities, such as two-step synthesis, cost-effectiveness, and long-lasting physical and chemical stability. Consequently, these molecules can be considered as promising alternatives to combat emerging SARS-CoV-2 variants.
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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 | 0.001 |
| 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".