Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as anticancer agents through comparative QSAR modeling approaches
Bibliographic record
Abstract
An indole ring system is considered as a versatile scaffold in the pharmaceutical field. In this article, comparative QSAR modeling (2D-QSAR, 3D-QSAR; kNN-MFA and CoMSIA) was performed on some Knoevenagel-type cytotoxic indole derivatives to understand the structural requirements for the cytotoxic property of these compounds. The 2D-QSAR model was statistically significant and imparted high predictive ability (nTrain = 30; R = 0.917; [Formula: see text] = 0.801; [Formula: see text] = 0.757; Q2 = 0.722; nTest = 9; [Formula: see text] = 0.799). A statistically significant 3D-QSAR kNN-MFA model (both with stepwise forward and simulated annealing model selection method) as well as a 3D-QSAR CoMSIA model was developed to identify the key chemical features associated with enhancing the cytotoxic activities of these indoles. The results suggest that the presence of bulky group in R position can cause better cytotoxic activities. Consequently, substitution with cyano group at X portion and cyano/ester/keto/sulphonyl features at Y position is favourable for the cytotoxicity. However, hydrophobic features in R′ region are unfavourable for the biological activity. The chemical and structural features identified from the study may provide important avenues to modulate the structure of these indoles to a desirable biological end point.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".