Modeling the Physical Properties of Cholera Treatment Drugs via Neighborhood Sum Degree-Based Topological Indices
Bibliographic record
Abstract
Cholera remains a global health challenge, which requires the optimization of treatment strategies, including the design of effective drugs. This study explores the utility of neighborhood sum degree-based topological indices (TIs) in predicting the physical properties of cholera treatment drugs through quantitative structure-property relationship (QSPR) modeling. Eight TIs, neighborhood first Zagreb, second Zagreb, hyper Zagreb, geometric-arithmetic, forgotten, harmonic, Randi´c and atom bond connectivity indices were evaluated using linear regression models across six physicochemical properties: boiling point, flash point, enthalpy of vaporization, molar refraction, polarization, and molar volume. Among all indices, neighborhood harmonic, sum connectivity, and atom bond connectivity indices stand out for their high R2 values and low standard errors in modeling molar refraction and polarization. For boiling point and flash point, the neighborhood first and second Zagreb indices, and randic index provide moderate predictive power. Some indices such as neighborhood hyper, second Zagreb and forgotten indices showed moderate performance for all physical properties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".