Structure-Based in Silico Study of 6-Gingerol, 6-Ghogaol, and 6-Paradol, Active Compounds of Ginger (Zingiber officinale) as COX-2 Inhibitors
Bibliographic record
Abstract
Ginger’s (Zingiber officinale) phenolic compounds, that are 6-gingerol, 6-shogaol, and 6-paradol, have been proven to show anti-inflammatory activity. The purpose of this paper was to discover whether these compounds are potential to be used as COX-2 inhibitors through structure-based in silico study, which is based on the character of the receptor. Docking was performed to the binding pockets of both COX-1 and COX-2 enzymes, to examine their selective character on COX-2. The binding pockets used in this project were the sites where flurbiprofen and SC-58, crystallized in the enzymes. The scoring value of the interaction of 6-gingerol, 6-shogaol, and 6-paradol with COX-1 were -7.40, -7.27, and -7.20 kcal/mol, while with COX-2 were -7.97, -8.10, and -7.80 kcal/mol, respectively. Ki value to COX-1 were 3.78, 4.66, and 5.30 ?M, while to COX-2 were 1.46, 1.16, and 1.93 ?M, respectively. We also calculated the selectivity index value of these compounds to COX-2 and resulted an interval of 0.2 to 0.4, which indicated that all tested compounds could be classified as preferential COX-2 inhibitors. It can be concluded that 6-gingerol, 6-shogaol, and 6-paradol could be developed as COX-2 inhibitors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".