Molecular Initiating Events of Bisphenols on Androgen Receptor-Mediated Pathways Provide Guidelines for <i>in Silico</i> Screening and Design of Substitute Compounds
Bibliographic record
Abstract
Bisphenols (BPs) have the potential to interfere with the androgen receptor (AR). However, in silico screening and substitute design were difficult because little was known about the mechanisms by which BPs interfere with AR-mediated molecular initiating events (MIEs). Here, the AR disrupting effects and associated mechanisms of 15 BPs were evaluated by in vitro assays and molecular dynamics simulations. AR-mediated MIEs, including ligand–receptor interactions and coregulator recruitment, might determine active versus inactive and agonist versus antagonist activities of BPs, respectively. Bisphenol E (BPE), BPF, and BPS with no binding effects were inactive, while all other BPs were AR antagonists. On the basis of their coregulator recruitment patterns and repositioning of helix 12, BPBP, BPC, and BPPH were passive antagonists that blocked coregulator recruitment, and their anti-androgenic potencies were correlated with ligand–receptor interactions; others were active antagonists that recruited corepressors, and their anti-androgenic potencies were correlated with ligand–receptor–corepressor interactions. A new method was developed for MIE-based in silico qualitative and quantitative evaluations of the potential of BPs to disrupt AR-mediated pathways, by which safer BPA substitutes with smaller and less hydrophobic connecting groups could be designed. The MIE-based in silico methods can be used to screen a wider range of chemicals and to design better substitutes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".