Novel selenium-based molecules as drug candidates for Alzheimer`s disease
Bibliographic record
Abstract
Although Alzheimer’s disease (AD) was first diagnosed over 100 years ago, the number of therapeutic options remains very limited, and the drug discovery process for AD is painstakingly slow. This is attributed to the complexity of the disease's pathophysiology. Consequently, AD research has shifted from a monotherapy approach into a multi-targeted approach where one molecule is able to hit multiple targets. Organoselenium compounds as multi-targeted drug ligands (MTDLs) have been developed as potential inhibitors of Aβ aggregation and to reduce oxidative stress in AD, and also to provide novel scaffolds for designing promising disease-modifying agents. By utilizing computational chemistry principles, organoselenium tricyclic scaffolds were designed that exhibited promising binding affinity, and efficiency toward Aβ protein, different organic chemistry procedures were applied to develop synthetic methods to obtain the target derivatives. Further studies include structure-activity relationship (SAR) optimization by carrying out in vitro fluorescence kinetic studies to determine the inhibition of Aβ40 aggregation, transmission electron microscopy (TEM) studies, evaluation of antioxidant properties, and cell culture studies to identify novel organoselenium derivatives as MTDLs. Preliminary studies demonstrate a significant reduction in the Aβ40 aggregation suggesting their application in the development of novel therapeutic agents for the treatment of AD.
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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) | 0.003 | 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".