Open drug discovery in Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) drug discovery has focused on a set of highly studied therapeutic hypotheses, with limited success. The heterogeneous nature of AD processes suggests that a more diverse, systems-integrated strategy may identify new therapeutic hypotheses. Although many target hypotheses have arisen from systems-level modeling of human disease, in practice and for many reasons, it has proven challenging to translate them into drug discovery pipelines. First, many hypotheses implicate protein targets and/or biological mechanisms that are under-studied, meaning there is a paucity of evidence to inform experimental strategies as well as high-quality reagents to perform them. Second, systems-level targets are predicted to act in concert, requiring adaptations in how we characterize new drug targets. Here we posit that the development and open distribution of high-quality experimental reagents and informatic outputs-termed target enabling packages (TEPs)-will catalyze rapid evaluation of emerging systems-integrated targets in AD by enabling parallel, independent, and unencumbered research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".