Deciphering the <i>in silico</i> molecular mechanism of coumestrol activity for uterine fibroids remedy: a promising estrogenic target drug candidate
Bibliographic record
Abstract
Uterine fibroids (UF) are reproductive conditions that occur as tumours in the womb. It is a gynecological outgrowth of diverse sizes often allied with infertility risks that might require surgery to reduce the complication in the worst-case scenario in women. Recent studies have uncovered that estrogen can induce and facilitate other target pathways’ action on target cells for UF’s pathogenesis, among the targets probed for pharmaceutical intervention. This study screens the interaction effects of 32 phytochemicals from indigenous and adopted potent Chinese plants and herbs; Chamomile, Pomegranate, Red clover, Cinnamomum, and Date palm, against estrogen receptor alpha (ESRα) to serve for anti-UF drug candidates using in silico tools through the molecular mechanisms. The interaction identifies coumestrol as the best-docked candidate (−9.6 kcal/mol) with a correlation to the binding free energy (−30.487 kcal/mol) as compared to the standard drug tamoxifen (−9.3 kcal/mol; −46.928 kcal/mol). The downstream post-docking evaluation reveals coumestrol to have excellent pharmacokinetics, drug-likeness, leadlikeness (no violation), less toxic (LD50; 2991 mg/kg), and highly interactive with ESRα. Coumestrol was top-ranked for ESRα (1QKU) target by PharmMapper among 300 human protein targets, with a z-score of 1.19368. The density functional theory (DFT) and dynamic simulation of 200 ns reveal regions of coumestrol structure and its complex that contribute to the chemical reactivity, stability, flexibility, and compactness of druggability. Ultimately, coumestrol emerged as a potential candidate suitable for anti-UF management, therefore future direction for its application should be on the design and synthesis of new structural derivatives for further in silico, in vitro, and in vivo studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".