Quantitative Structure‐Activity Relationship (QSAR) Study with a Series of 17α‐Derivatives of Estradiol: Model for the Development of Reversible Steroid Sulfatase Inhibitors
Bibliographic record
Abstract
Abstract Steroid sulfatase (STS) is the steroidogenic enzyme responsible for the hydrolysis of different sulfated steroids into their corresponding hydroxylated forms. This enzyme attracts our attention for its potential role in the growth of hormone‐dependent breast and prostate tumors by the transformation of inactive sulfated precursors (which are very abundant in the blood) into active sex steroids. In order to identify the parameters responsible for good affinity with the active enzyme site and thus producing a good reversible STS inhibitor, we have built a quantitative structure‐activity relationship (QSAR) model by using MDL‐QSAR software which analyzes the molecules through more than 400 molecular descriptors. A total of 65 derivatives in position 17α of estradiol with their corresponding IC50 values were used to create our QSAR model. The linear regression converged through an optimization process to a relatively simple equation described by 4 molecular descriptors (Log P, nelem, κ0 and κα3). Virtual screening of approximately 200 molecules then enabled us to direct the synthesis of new reversible STS inhibitors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".