Liquiritin from Glycirrhyza Glabra L (Fabaceae) - a Natural Derived Drug, as a Potential Inhibitor for SARS-CoV-2
Bibliographic record
Abstract
Novel Corona virus-2 (Covid-19) is spreading and causing major damage around the globe and constantly increasing daily. There is a prerequisite of expeditious development of safe and efficient drugs for such a contagious disease. In this regard, utilization of a computational approach with an aim to provide potential enzyme inhibitors derived from natural resources will give a providential therapy. The present study investigated one of the promising plants namely Glycyrrhiza glabra L. It has various medicinal properties viz. anti-inflammatory, anti-cancer, anti-demulcent, expectorant, etc. In-Silico Analysis of liquiritin against SARS-CoV-2 Mpro was carried out using Autodock 4.2.6 and results were compared with presently prescribed drugs i.e. dexamethasone, remdesivir, hydroxychloroquine, and azithromycin. The binding energy of liquiritin was found to be -6.62 kcal/mol. It shows presence of hydrogen bond, hydrophobic interaction and electrostatic interaction with six active residues THR26, GLY143, CYS145, HIS 164, GLU166, and GLN189. Comparative studies investigated that dexamethasone, remdesivir, hydroxychloroquine, and azithromycin have four (THR26, GLY143, CYS145, GLU166), three (CYS145, GLU166, GLN189), four (GLY143, CYS145, HIS 164, GLN189) and two (GLU166, GLN189) identical active residues, respectively. The present study recommended liquiritin as a potential candidate against SARS-CoV-2 as it is naturally derived and has tremendous traditional usage against various diseases. However, in-vitro and in-vivo studies are required to prove its efficacy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".