AD Informer Set: Chemical tools to facilitate Alzheimer's disease drug discovery
Bibliographic record
Abstract
Introduction: The portfolio of novel targets to treat Alzheimer's disease (AD) has been enriched by the Accelerating Medicines Partnership Program for Alzheimer's Disease (AMP AD) program. Methods: Publicly available resources, such as literature and databases, enabled a data-driven effort to identify existing small molecule modulators for many protein products expressed by the genes nominated by AMP AD and suitable positive control compounds to be included in the set. Compounds contained within the set were manually selected and annotated with associated published, predicted, and/or experimental data. Results: We built an annotated set of 171 small molecule modulators targeting 98 unique proteins that have been nominated by AMP AD consortium members as novel targets for the treatment of AD. The majority of compounds included in the set are inhibitors. These small molecules vary in their quality and should be considered chemical tools that can be used in efforts to validate therapeutic hypotheses, but which will require further optimization. A physical copy of the AD Informer Set can be requested on the Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD) website. Discussion: Small molecules that enable target validation are important tools for the translation of novel hypotheses into viable therapeutic strategies for 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.147 | 0.081 |
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".