From jeopardy champion to drug discovery; semantic similarity artificial intelligence
Bibliographic record
Abstract
We have employed artificial intelligence to streamline the small molecule drug screening pipeline and identified the cholesterol-reducing compound probucol in the process. Probucol augmented mitophagy and prevented loss of dopaminergic neurons in flies and zebrafish challenged with mitochondrial toxins. Further dissection of the mechanism of action led to the identification of ABCA1, the target of probucol, as a mitophagy modulator. Probucol treatment regulates lipid droplet dynamics during mitophagy and ABCA1 is required for these effects. Here we will summarize the combination of in silico and cell-based screening that led us to identify and characterize probucol as a compound that enhances mitophagy and include thoughts about future directions for the topics explored in our study.Abbreviations: ABCA1: ATP binding cassette transporter protein 1; ATP: Adenosine tri-phosphate; CCCP: carbonyl cyanide m-chlorophenylhydrazone; DsRed: Discosoma red; FDA: Food and drug administration; GFP: Green fluorescent protein; LAMP: lysosome-associated membrane glycoproteins; LD: Lipid droplet; PD: Parkinson’s disease; PINK: PTEN-induced kinase
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".