Molecular modeling and docking calculations of 4-acyloxy-biphenyl-4′-<i>N</i>-butylcarbamates as potential inhibitors of human butyrylcholinesterase
Bibliographic record
Abstract
The kinetic studies and drug designs of butyrylcholinesterase play an important role in the development of Alzheimer’s disease therapeutics. In this research, automated docking studies were performed to provide useful insights into butyrylcholinesterase inhibition binding modes with designed 4-acyloxy-biphenyl-4′-N-butylcarbamates (compounds 1–8). Moreover, several significant linear correlations between experimental and calculated docking results are observed. Among compounds 1–7, compound 3, which exhibits the strongest hydrophobicity and has four carbonyl hydrogen bindings, shows the highest binding affinity (Ki = 1.4 μmol/L) with a binding energy of −7.99 kcal/mol. The observed linear correlation of experimental and calculated inhibition constants (Ki) indicates that the molecular docking results are reliable. Moreover, a good linear correlation is observed between calculated binding energies and experimental pKi. The experimental Hansch hydrophobicity constants (π values) are also correlated with the docked binding energy. This study reveals important correlations between butyrylcholinesterase experimental and docking results that contribute to the kinetic based identification of antagonists for the treatment of Alzheimer’s disease. Furthermore, these docked models provide important insights into a potential series of 4,4′-biphenol-based inhibitors of butyrylcholinesterase.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".