Exploring the Potentials of Phytochemicals for Targeting Coronavirus
Bibliographic record
Abstract
Abstract: Since the initial outbreak in December 2019, the COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to more than 3 million deaths worldwide. There is an urgent need for developing a potential therapy against SARS-CoV-2. Exploring the potentials of phytochemicals towards inhibition of SARS-CoV-2 proteins remains of significant scientific interest. The therapeutic values of phytochemicals in the treatment of diseases, such as viral infections, are known for a long time. In this review, we present a brief overview of the past experimental and computational efforts on evaluating phytochemicals against SARS coronaviruses, an earlier coronavirus strain. We discuss natural metabolites of different structural and chemical scaffolds, including polyphenols, flavonoids, and phytosterols, which can be promising compounds for screening against the currently evolving SARS-CoV-2 virus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".