QSPR Modeling of Anticancer Drugs Using Uphill Topological Indices
Bibliographic record
Abstract
Over the past thirty years, cancer has affected more than ten million people worldwide annually. While diverse treatments exist (chemotherapy, surgery, radiation, immunotherapy, stem cell transplants), anti-cancer drugs remain essential. This research introduces five novel uphill topological indices derived from molecular graphs to establish Quantitative Structure-Property Relationships (QSPR) for ten essential anticancer drugs: carmustine, convolutamine F, raloxifene, tambjamine K, pierocellin B, caulibugulone E, convolutamide A, daunorubicin, deguelin, and podophyllotoxin. Power regression analysis correlated these indices with six experimental properties: boiling point (BP), melting point (MP), enthalpy (E), molar refraction (MR), molar volume (MV), and surface tension (ST). Results demonstrate statistically significant correlations (p < 0.05), with the uphill sigma index (UPSIG) emerging as the optimal predictor for BP (R2 = 0.929), MP (R 2 = 0.907), and E (R2 = 0.828), while the uphill Albertson index (UPAL) excelled for MV (R2 = 0.918). Predictive validity was confirmed via multilinear regression (R2 > 0.934 for BP/E/MV/MR), establishing these indices as powerful tools for rational anticancer drug design.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".