Integrative <i>in silico</i> evaluation of the antiviral potential of terpenoids and its metal complexes derived from <i>Homalomena aromatica</i> based on main protease of SARS-CoV-2
Bibliographic record
Abstract
Abstract Substantial research is currently conducted focusing on the development of promising antiviral drugs employing in silico screening and drug repurposing strategies against SARS-CoV-2. The current study aims at identifying lead molecules targeting SARS-CoV-2 by the application of in silico and molecular dynamics (MD) approaches from phytoconstituents present in Homalomena aromatica. The main protease (Mpro) enzyme of SARS-CoV-2 is taken as the target protein to perform the docking analysis of 71 molecules reported from H. aromatica by the application of different modules of Discovery Studio 2018. Five molecules were taken as prospective leads namely dihydrocuminaldehyde, p-cymen-8-ol, cuminaldehyde, p-cymene, and cuminol. In the absence of known inhibitors, a comparative study was performed with the compounds reported in the literature and potent terpenoid–metal complexes were taken into account based on known efficacy as anti-viral molecules. After performing the docking studies with Mpro enzyme of SARS-CoV-2, it was observed that the –CDocker Energy of cuminaldehyde thiosemicarbazone was 29.152, indicating a significant affinity toward Mpro. The same was also supported by the MD study. Taken together, our results provided in silico evidence that secondary metabolites derived from H. aromatica could be employed as potent antiviral agents targeting SARS-CoV-2. Our findings warrant further validation of their in vitro and in vivo efficacies prior to their development into bona fide therapeutic agents.
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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 | 0.001 |
| 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) | 0.004 | 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".