An Updated Steroid Benchmark Set and Its Application in the Discovery of Novel Nanomolar Ligands of Sex Hormone-Binding Globulin
Bibliographic record
Abstract
A benchmark data set of steroids with known affinity for sex hormone-binding globulin (SHBG) has been widely used to validate popular molecular field-based QSAR techniques. We have expanded the data set by adding a number of nonsteroidal SHBG ligands identified both from the literature and in our previous experimental studies. This updated molecular set has been used herein to develop 4D QSAR models based on "inductive" descriptors and to gain insight into the molecular basis of protein-ligand interactions. Molecular alignment was generated by means of docking active compounds into the active site of the SHBG. Surprisingly, the alignment of the benchmark steroids contradicted the classical ligand-based alignment utilized in previous CoMFA and CoMSIA models yet afforded models with higher statistical significance and predictive power. The resulting QSAR models combined with CoMFA and CoMSiA models as well as structure-based virtual screening allowed discovering several low-micromolar to nanomolar nonsteroidal inhibitors for human SHBG.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".