Additional file 2 of Characterization of novel small molecule inhibitors of estrogen receptor-activation function 2 (ER-AF2)
Bibliographic record
Abstract
Supplementary Material 2: Supplementary Figure S2. Cell-based data used for in silico similarity searches. (A) The percentage of ER transcriptional inhibition after treatment of T47D-kbluc cells with 10 µM of each of the 512 compounds, identified through in silico docking. (B) Counter screen to determine the effect of the compounds on inhibition of luciferase in ER-negative PC3m-luc cells. (C) Viability of current hits in ER-negative PC3m-luc cells to eliminate toxic compounds. (D) Structures of the four selected chemotypes, VPC-260156, VPC-260241, VPC-260263, VPC-260277, highlighted in red in panels A-C. (E) Dose-response inhibition of transcriptional activity measured with luciferase reporter assay in T47D-KBluc cells following treatment of tested compounds for 24 h. (F) Dose-response inhibition of cell viability measured PrestoBlue in T47D and ER-negative MDA-MB-468 cells following treatment of tested compounds for 72 h. (G) PLA showing interaction of ER and SRC3 in T47D cells with quantification of the corresponding PLA signal/nuclei (H). P values are indicated by stars: ns ≥ 0.05, * 0.01 to 0.05, ** 0.001 to 0.01, *** 0.0001 to 0.001, **** <0.0001.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.821 | 0.164 |
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".