Generation of the AD Informer Set: Chemical tools to facilitate Alzheimer’s disease drug discovery
Bibliographic record
Abstract
Abstract Background Currently, treatments for Alzheimer’s disease (AD) predominantly include acetylcholinesterase inhibitors, which address the symptomatic effects of the disease. The discovery of disease modifying interventions in the development and progression of AD are critical as there are currently no drugs or biologics which can elicit disease modifying effects. Promisingly there is an increase in the number of novel therapeutic strategies targeting AD that have progressed to clinical trials, and interrogation of targets implicated in AD pathology remains an area of extensive research. Method To assist the discovery of novel strategies that slow or halt AD pathology we have generated the AD Informer Set, the first iteration of which contains more than 150 chemical tools. We will make solutions of the physical set as well as all associated data available to the scientific community in 2021. Further data is being collected on compound kinetic solubility, pharmacokinetic properties, potential off‐target liabilities, and AD‐specific readouts, including mitochondrial metabolism, oxidative stress and glycolysis, and effects on p‐Tau and Aβ expression levels. Result The AD Informer Set includes compounds for targets that have AD implicated biology, either well validated or recently suggested, and contains relevant information about the gene, compound structure and activity, and associated AD phenotypes. 1, 2 Conclusion The AD Informer Set will be shared with the scientific community at large to qualify assays, interrogate target validity in AD models, and to provide positive controls to aid in the discovery of new chemical entities. References: (1) Hodes RJ, Buckholtz N. Accelerating Medicines Partnership: Alzheimer's Disease (AMP‐AD) Knowledge Portal Aids Alzheimer's Drug Discovery through Open Data Sharing. Expert Opin Ther Targets. 2016, 20, 389‐91. PMID: 26853544. (2) Mullard A. NIH launches open science Alzheimer initiative. Nat Rev Drug Discov. 2019, 18, 895. PMID: 31780852.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".