CoMFA and CoMSIA Studies on 1,3-Bis(benzylidene)-3,4-dihydro-1<i>H</i>-naphthalen-2-one, 2,6-Bis(benzylidene)cyclohexanone, and 3,5-Bis(benzylidene)-4-piperidone Series of Cytotoxic Compounds
Bibliographic record
Abstract
A number of 1,3-bis(benzylidene)-3,4-dihydro-1H-naphthalen-2-ones, 2,6-bis(benzylidene)cyclohexanones, and 3,5-bis(benzylidene)-4-piperidones possess significant potencies toward L1210, Molt 4/C8, and CEM cell lines. The objective of the current 3D QSAR study is to discover some of the structural parameters which govern cytotoxic potencies. The CoMFA models with steric and electrostatic fields provided satisfactory statistical data [(r2cv = 0.485, r2ncv = 0.834, r2pred = 0.591), (r2cv = 0.532, r2ncv = 0.850, r2pred = 0.729), and (r2cv = 0.561, r2ncv = 0.864, r2pred = 0.666)] in regard to the cytotoxic potencies observed toward L1210, Molt 4/C8, and CEM cell lines, respectively. The CoMSIA model with steric, electrostatic, hydrophobic, and H-bond donor fields exhibited r2cv = 0.513, r2ncv = 0.833, and r2pred = 0.562 for cytotoxic activity toward L1210 cells, while the best CoMSIA models were obtained by a combination of steric, electrostatic, and hydrophobic fields which yielded statistically significant data [(r2cv = 0.531, r2ncv = 0.828, r2pred = 0.652) and (r2cv = 0.560, r2ncv = 0.841, r2pred = 0.729)] to explain the cytotoxicity toward Molt 4/C8 and CEM cells, respectively. The information obtained from the CoMFA and CoMSIA 3D contour maps can be used in the design of more potent cytotoxins.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".