Secondary metabolites from spice and herbs as potential multitarget inhibitors of SARS-CoV-2 proteins
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been responsible for the current global pandemic that has caused a death toll of >1.12 million worldwide and number continues to climb in several countries. Currently, there are neither specific antiviral drugs nor vaccines for the treatment and prevention of COVID-19. We screened <i>in silico</i>, a group of natural spice and herbal secondary metabolites (SMs) for their inhibition efficacy against multiple target proteins of SARS-CoV-2 as well as the human angiotensin-converting enzyme 2 protein. Docking and simulation results indicated that epicatechin, embelin, hesperidin, cafestol, murrayanine and murrayaquinone-A have higher inhibition efficacy over at least one of the known antiviral drugs such as Hydroxychloroquine, Remdesivir and Ribavirin. Combination of these potentially effective SMs from their respective plant sources was analysed, and its absorption and acute oral toxicity were examined in Wistar rats and classified as category 5 as per the Globally Harmonized System. The identified SMs may be useful in the development of preventive nutraceuticals, food supplements and antiviral drugs. Communicated by Ramaswamy H. Sarma
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.155 | 0.021 |
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; both teacher heads agree on what is shown here.
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".