Molecular Modeling Study of PPARr Agonists: Dehydro-Di-Isoeugenol, Macelignan, Pioglitazone, Netoglitazone, and Rosiglitazone as Antidiabetic Drugs
Bibliographic record
Abstract
The peroxisome proliferator-activated receptors (PPARs) are ligand-activated trasncription factors belonging to the nuclear receptor family. The objective of this study is to analyze the molecular aspects of PPARr agonists which used to design of new antidiabetic drugs. The analysis method was comparing the interactions of ligands in the ligand binding domain of the PPARr. This analysis showed that most known agonists of PPARr interacted via hydrogen bond with Tyr473. Pioglitazone showed three hydrogen bonds with His323 and Tyr473. Netoglitazone showed four hydrogen bonds with Ser289, His323, His449, and Tyr473. Rosiglitazone showed five hydrogen bonds with Ser289, His323, His449, and Tyr473. AZ72, an agonist of PPARr and r showed five hydrogen bonds with Ser289, His323, His449, and Tyr473. Molecular modeling was performed by redocking pioglitazone and rosiglitazone using AutoDock Vina. Docking showed that both pioglitazone (Ki 0.22 uM) and rosiglitazone (Ki 0.70 uM) occupied their origin sites and interacted with Tyr473. Docking simulation was also performed between dehydro-di-isoeugenol and macelignan to visualize the interaction with PPARr. These two compounds are found in nutmeg’s seed (Myristica fragrans Hout) that have been proven had antidiabetic activity in vitro. It can be concluded that agonists of PPARr should have hydrogen bond donor and acceptor groups for interacting with Tyr473. Tyr473 might be a critical site of interaction between the PPARr ligand binding domain and its agonists.
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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 | 0.001 | 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".