Efficient recognition of steroids by planar aromatic molecules: a novel biomolecular recognition motif and its potential applications
Bibliographic record
Abstract
Traditionally, functional groups attached to the backbone of steroid molecules were seen as the key factors in their molecular recognition and, consequently, their physiological function.However, a systematic study of the cocrystals of steroids with electron-rich, planar aromatic molecules (arenes) by our group has recently revealed the ••• interaction: a previously undescribed interaction mode of steroids involving the -face of a steroid and theelectron system of aromatic molecules.Progesterone was found to reliably engage in ••• interaction with various arenes, whereas other steroids studied so far suggest a strong dependence of this interaction on the fine structural details of the steroid backbone.This mimics the steroid behavior in the biological systems, where small structural differences give rise to significantly different biological functions.We set out to pursue the cocrystallization of progesterone with a variety of polyaromatic hydrocarbons and heterocycles using the mechanochemical solid-state screening methods developed in our group.Furthermore, we sought to expand the set of steroid cocrystal formers towards biologically and pharmaceutically relevant adrenosterone (a weak androgen), cholest-4-en-3-one (metabolite of cholesterol), exemestane (anticancer drug) and levonorgestrel (used in birth control), all of which exhibit a degree of structural similarity with progesterone.Finally, we investigated the possibility of combining the ••• interaction with known interactions such as hydrogen and halogen bonding to engineer increasingly complex molecular solids.This presentation will outline our initial findings, which confirm the reliability of the ••• interactions and establish its applicability to steroid molecules beyond progesterone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".