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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".