In Silico Discovery and Predictive Modeling of Novel Acetylcholinesterase (AChE) Inhibitors for Alzheimer's Treatment
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Alzheimer's disease, akin to coronary artery disease of the heart, is a progressive brain disorder driven by nerve cell damage. METHODS: This study utilized computational methods to explore 14 anti-acetylcholinesterase (AChE) derivatives (1 ̶ 14) as potential treatments. By scrutinizing their interactions with 11 essential target proteins (AChE, Aβ, BChE, GSK-3β, MAO B, PDE-9, Prion, PSEN-1, sEH, Tau, and TDP-43) and comparing them with established drugs such as donepezil, galantamine, memantine, and rivastigmine, ligand 14 emerged as notable. During molecular dynamics simulations, the protein boasting the strongest bond with the critical 1QTI protein and exceeding drug-likeness criteria also exhibited remarkable stability within the enzyme's pocket across diverse temperatures (300- 320 K). In addition, we utilized density functional theory (DFT) to compute dipole moments and molecular orbital properties, including assessing the thermodynamic stability of AChE derivatives. RESULT: investigations. CONCLUSION: Ligand 14 thus emerges as a promising candidate in the fight against Alzheimer's disease.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it