Alzheimer's Disease: Emerging Trends in Small Molecule Therapies
Bibliographic record
Abstract
Alzheimer's disease (AD) is a highly complex and rapidly progressive neurodegenerative disorder characterized by the systemic collapse of cognitive function and formation of dense amyloid plaques and neurofibrillary tangles. AD pathology is derived from the cholinergic, amyloid and tau hypotheses, respectively. Current pharmacotherapy with known anti-cholinesterases, such as Aricept® and Exelon®, only offer symptomatic relief without any disease-modifying effects. It is now clear that in order to prevent the rapid progression of AD, new therapeutic treatments should target multiple AD pathways as opposed to the traditional "one drug, one target" approach. This review will focus on the recent advances in medicinal chemistry aimed at the development of small molecule therapies that target various AD pathological routes such as the cholinesterases (AChE and BuChE), amyloidogenic secretases (β/γ- secretase), amyloid-β aggregation, tau phosphorylation and fibrillation and metal-ion redox/reactive oxygen species (ROS). Some notable ring templates will be discussed along with their structure-activity relationship (SAR) data and their multiple modes of action. These emerging trends signal a paradigm shift in anti-AD therapies aimed at the development of multifunctional small molecules as disease-modifying agents (DMAs).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".