Possible Drug Candidates for Alzheimers Disease Deduced from Studying their Binding Interactions with α7 Nicotinic Acetylcholine Receptor
Bibliographic record
Abstract
Dysfunction in alpha7 nicotinic acetylcholine receptor (nAChR), a member of the Cys-loop ligand-gated ion channel superfamily, is responsible for attentional and cognitive deficits in Alzheimer's disease (AD). To provide useful information for finding drug candidates for the treatment of AD, a study was carried out according to the following procedures. (1) DMXBA, a partial agonist of the alpha7 nAChR, was used as a template molecule. (2) To reduce the number of compounds to be considered, the similarity search and flexible alignment were conducted to exclude those molecules which did not match the template. (3) The molecules thus obtained were docked to alpha7 nAChR. (4) To gain more structural information, the molecular dynamics (MD) simulations were carried out for 9 most favorable agonists obtained by the aforementioned docking studies. (5) By analyzing the hydrogen bond interaction and hydrophobic/hydrophilic interaction, the following seven compounds were singled out as possible drug candidates for AD therapy: gx-50, gx-51, gx-52, gx-180, open3d-99008, open3d-51265, open3d-60247.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.004 | 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".