Rational questing for inhibitors of endothelin converting enzyme-1 from <i>Salvia miltiorrhiza</i> by combining ligand- and structure-based virtual screening
Bibliographic record
Abstract
In this study, a virtual screening approach based on pharmacophore and molecular docking was proposed to identify endothelin converting enzyme-1 (ECE-1) (EC 3.4.24.71) inhibitors from Salvia miltiorrhiza. First, the pharmacophore models were generated to recognize the common features of the ECE-1 inhibitors. The models were validated by a test database composed by a set of compounds known as ECE-1 inhibitors and nonactive compounds and proven to be successful in discriminating active and inactive inhibitors. Then, the best pharmacophore model was used to screen the compounds from S. miltiorrhiza. Furthermore, the Surflex-Dock procedure was used for molecular docking. All compounds from S. miltiorrhiza were docked into the active site of the target protein. An empirical scoring function was used to evaluate the affinity of the compounds and the target protein. Comparing the virtual screening results based on pharmacophore and molecular docking, respectively, 11 communal compounds with higher QFIT and docking score were hit, and the activity of some compounds was validated in the literature. The binding modes between these compounds and the ECE-1 binding site were predicted and used to identify the key interactions that contribute to the inhibitory activity of ECE-1 activity. The results show that the two methods have good consistency and can be validated and supplemented with each other.
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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 | 0.001 |
| 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".