Structural requirements of salicylaldehyde benzoylhydrazones and their Cu(II) complexes for anticancer activity
Bibliographic record
Abstract
Salicylaldehyde benzoylhydrazone (H2sb) has a variety of biological activities including anticancer activity. The Cu(II) complexes of H2sbs possess enhanced anticancer activity as compared with their free ligands. A quantitative structure–activity relationship (QSAR) analysis was performed on a series of H2sb ligands and their corresponding Cu(II) complexes to capture the structural requirements that are responsible for the bioactivity. The predictive QSAR models were developed using statistical techniques such as multiple linear regression (MLR) and principal component regression analysis (PCRA). We used different combinations of various descriptors such as a physicochemical descriptor, electrotopological state atom (ETSA) indices, and descriptors derived from density functional theory (DFT) calculations. The DFT-derived descriptors used for QSAR analysis are HOMO and LUMO energies, atomic charges, chemical potential, and hardness. Our developed models showed the importance of the lipophilicity index (ClogP), ETSA indices, and atomic charges for anticancer activities of the H2sb analogs and their Cu(II) complexes. In addition, our MLR models revealed that, while the global lipophilicity index and hardness are important for anticancer activity of H2sb ligands, chemical potential and HOMO energy are important for the anticancer activity of Cu(II) complexes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".