Investigating emodin derivatives against SARS-CoV-2 found in medicinal herbs
Bibliographic record
Abstract
SARS-CoV-2 continues to mutate and circulate at alarming levels around the world and is exemplified by the emergence of the new variant JN.1. The emergence of novel SARS-CoV variants underscores the urgency for the development of new drugs. Phytochemicals, known for their safety and efficacy, have been widely used to address various ailments. This study aims at identifying the anti-SARS-CoV potential of emodin, a bioactive anthraquinone, and its derivatives. Key proteins, including hemagglutinin-esterase(HE), papain-like protease(PLpro), major protease(3CLpro), non-structural protein (nsp3) and spike protein(S) of SARS-CoV, were used as protein targets for the virtual screening of 110 emodin derivatives (ED) and remdesivir a proven antiviral drug. Among the 110 derivatives, ED21, ED25, ED5 inhibited HE, 3CLpro and S protein, respectively. ED29 inhibited both PLpro and nsp3 with high binding affinity. Similar receptor binding sites were occupied by remdesivir and emodins.These emodins are bound to similar amino acids in receptors as compared to remdesivir. Further dynamic simulations studies with ED21-HE, ED25-3CLpro, ED5-S, ED29-PLpro and ED29-nsp3 complexes showed all of them are stable with minimum root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent-accessible surface area (SASA) and radius of gyration (Rg); and are comparable to APO protein. All the ligands accepted Lipinski’s rule of five. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis revealed only ED21 and ED5 as promising druglike candidates that can have high therapeutic value and no side effects. These in-silico findings contribute to the development of potent SARS-CoV-2 inhibitors, potentially advancing the quest for effective antiviral drugs.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".