Structural and Computational Insights into the Angiotensin II Type 1 Receptor: Advances in Antagonist Design and Implications for Hypertension Therapy (2020–2024)
Bibliographic record
Abstract
The renin–angiotensin–aldosterone system (RAAS) is essential for controlling blood pressure and maintaining fluid balance, driving significant structural changes throughout the cardiovascular system, including the heart and blood vessels. As a result, the RAAS is a key therapeutic target for various chronic cardiovascular diseases, ranging from arterial hypertension (AH) to heart failure (HF). In this review, one of our objectives is to describe the new evidence over the last 4 years regarding the RAAS. Moreover, we pay attention to the structure and function of the angiotensin II type 1 receptor (AT1R) and its role in hypertension, as well as define its active site. Later, we discuss the most potent, selective inhibitors of AT1 receptors, based on in vitro and in vivo experiments, from 2020 to 2024. Large peptide molecules, small non-peptide-like molecules, and sartan derivatives are analyzed. The low IC50 values of the entities that do not resemble sartans showcase the vast chemical space that can be explored for the creation of more potent antihypertensive medications. We have also employed computational chemistry tools in order to identify key molecular interactions between the compounds of the literature studied in order to elucidate the underlying reasons why these different molecules exhibit variations in their binding energies and overall potency.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".