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 in silico, 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.021 | 0.008 |
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".