P1‐074: Designing Small Molecules as Pharmacological Tools to Study Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is a complex neurodegenerative disorder characterized by loss of memory, cognition and dementia. The pathophysiology of AD is complex and is not clearly understood. Some major hypotheses of AD include the cholinergic dysfunction, amyloid cascade, tau hyperphosphorylation and oxidative stress. In this regard, we are developing small molecules as chemical and pharmacological tools to study the mechanisms of the amyloid cascade hypothesis of AD. The ultimate goal is to develop small molecules as potential disease modifying agents with ability to target multiple pathways associated with AD pathophysiology as opposed to the traditional “one drug, one target” approach. Small molecules were designed using computational chemistry tools. Compound libraries were synthesized and evaluated using biochemical/biophysical techniques including Aβ40/42 aggregation kinetics and transmission electron microscopy (TEM). Computational studies were conducted to understand the forces involved in ligand-protein aggregate binding interactions. A large library of small molecules with diverse chemical features and core ring scaffolds were synthesized. The biological assay screening studies have identified a number of monocyclic, bicyclic and tricyclic rings that exhibit Aβ-aggregation inhibition as confirmed by aggregation kinetics assay and TEM studies. Computational studies suggest that the binding of these small molecules to Aβ-aggregates including dimers, oligomers and fibrils can stabilize the complex and prevent further aggregation and their accumulation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".