Evaluating Efficacy In Vitro and In Vivo of Small Molecules Predicted by Artificial Intelligence to Reduce Alpha-Synuclein Oligomers
Bibliographic record
Abstract
Background: Parkinson’s disease (PD) is an incurable neurodegenerative disorder characterized by dopaminergic neuron loss and α-synuclein aggregation. Using artificial intelligence, we previously generated in silico predictions of approved drugs that may inhibit formation of α-synuclein oligomers, which are key mediators of neurodegeneration. Methods: I tested forty compounds highly ranked by the in silico predictions using an in vitro luciferase protein-fragment complementation assay. I then examined the positive hits using an in vivo behavioural assay of α-synuclein-mediated dopaminergic neuron dysfunction in C. elegans. Results: I identified ten compounds which significantly reduced α-synuclein oligomers. Four of these compounds were cytotoxic and thus excluded from further evaluation. I found five of the compounds rescued abnormal motor behaviour. Of these remaining compounds, rifabutin and rapamycin reduced α-synuclein oligomerization in rat primary neuronal cultures. Interpretation: Our combined in silico, in vitro, and in vivo approach discovered compounds that may be repurposed for disease-modifying PD treatments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".