Receptor‐attached amphiphilic terpolymer for selective drug recognition in aqueous solutions
Bibliographic record
Abstract
In the present work, a combination of binding studies and molecular docking were employed to demonstrate drug encapsulation and host-guest chemistry in self-assembled micelles consisting of amphiphilic terpolymers. The terpolymer is composed of poly(3-sulfopropyl methacrylate), as the hydrophilic component, poly(n-dodecyl acrylate), as the hydrophobic component and poly(barbiturate receptor), as the component for drug recognition. The combined approach was tested on four model compounds from the family of barbiturates, phenobarbital, mephobarbital, secobarbital, and thiopental, chosen based on their differential hydrogen bonding capabilities. Drug encapsulation and hydrogen-bonding based recognition within the micellar core of the receptor-terpolymer was demonstrated by micellar electrokinetic chromatography. The resulting trends in the binding affinity of the barbiturates to the receptor-terpolymer, were correlated to the trends obtained from computational docking simulations. This receptor-modified polymeric micelle is intended to serve as a model for the design of novel, versatile, and highly selective molecular scaffolds that will provide suitable environment for host-guest chemistry and act as simplified mimics to more complex biological systems.
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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) | 0.001 | 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".