A molecular dynamics study of conformations of beta-cyclodextrin and its eight derivatives in four different solvents
Bibliographic record
Abstract
Understanding the atomic level interactions and the resulting structural characteristics is required for developing beta-cyclodextrin (βCD) derivatives for pharmaceutical and other applications. The effect of four different solvents on the structures of the native βCD and its hydrophilic (methylated βCD; MEβCD and hydroxypropyl βCD; HPβCD) and hydrophobic derivatives (ethylated βCD; ETβCD) was explored using molecular dynamics (MD) simulations and solvation free energy calculations. The native βCD, 2-MEβCD, 6-MEβCD, 2,6-DMβCD, 2,3,6-TMβCD, 6-HPβCD, 2,6-HPβCD and 2,6-ETβCD in non-polar solvents (cyclohexane; CHX and octane; OCT) were stably formed in a symmetric cyclic cavity shape through their intramolecular hydrogen bonds. In contrast, βCDs in polar solvents (methanol; MeOH and water; WAT) exhibited large structural changes and fluctuations leading to significant deformations of their cavities. Hydrogen bonding with polar solvents was found to be one of the major contributors to this behavior: solvent-βCD hydrogen bonding strongly competes with intramolecular bonding leading to significant changes in the structural stability of βCDs. An exception to this is the hydrophobic 2,6-ETβCD which retained its spherical cavity in all solvents. Based on this, it is proposed that the 2,6-ETβCD can act as a sustained release drug carrier.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".