QSPR analysis of drugs for anti-hypertension using degree based topological indices through M-polynomial and NM-polynomial
Bibliographic record
Abstract
Abstract There are several uses for chemical graph theory in experimental science, medicine, drug development, and clinical studies. The statistical characteristics of medications have been developed and determined by topological descriptors. To extract a quantitative structural property/activity relationship (QSPR/QSAR), these indices can be employed alone or in conjunction with other numeric descriptors. Scientists are interested in investigating the chemical network's topology through QSPR investigations, employing specific mathematical constants and parameters derived from the molecular structures of networks. This paper focuses on using the M-polynomials and NM-polynomials of nine anti-hypertensive drugs to compute several degree-based topological indices. It is further shown that these topological descriptors have an excellent correlation with the physicochemical properties of considered anti-hypertensive drugs. Different statistical parameters are analyzed based on the collected results and conclusions are made for each parameter. Researchers exploring drug science in the pharmaceutical sector can utilize the findings to acquire a better understanding of the physical characteristics of newly discovered drugs that are used to treat various diseases. Mathematics Subject classification (2020): 92E10; 05C09; 05C31; 05C90; 05C92
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".