Integrating regression and classification-based QSARs with molecular docking analyses to explore the structure-antiaromatase activity relationships of letrozole-based analogs
Bibliographic record
Abstract
Aromatase is a multienzyme complex responsible for the biosynthesis of estrogen and its level has been found higher in breast cancer. Although the designing strategy of aromatase inhibitors (AIs) has continued for more than four decades, it may still be in demand to design highly effective and less toxic AIs. In this study, several chemometric approaches have been used to explore the important structural features of a series of letrozole-based analogs for their promising aromatase inhibitory activity. All techniques are statistically validated individually and in turn, validated with each other along with the structure–activity relationship (SAR) observations. The imidazole ring has been found to interact with the heme iron, whereas the triazole ring system has not shown any interaction. Moreover, imidazole function is better than 1,2,3-triazole, whereas 1,2,3-triazole is better than the 1,2,5-triazole ring system. Additionally, a bulky aryl substitution in the azole ring along with the orientation of the azole nitrogens and the cyanophenyl function has an essential role in the inhibition of aromatase. Furthermore, a cyano group substituted at the phenyl moiety interacts with Arg115, Met374, and Ser478 at the enzyme active site to form hydrogen bonding interactions. These observations are useful for designing potential AIs in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| 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".