Pre-registered, publicly scored AI ranking of repurposing candidates for cancer
AI systems claim to find new uses for old drugs, but their predictions are rarely tested fairly. Publish their cancer predictions in advance and score them against trial results.
Knowledge-graph and language-model approaches to repurposing (Every Cure, funded by ARPA-H; academic systems such as those built on Hetionet and Open Targets) generate ranked lists of drug-disease pairs, but their forward-looking accuracy in oncology is unknown because predictions are published selectively after the fact. The proposal is a public benchmark: each participating system deposits time-stamped ranked predictions for defined cancer indications; a neutral body scores them annually against subsequent randomised trial results and target trial emulations, and the repurposing fund preferentially trials candidates on which independent systems agree.
- No incentive to repurpose cheap drugs · Old, cheap drugs with anti-cancer signals never get the trials they need because no one profits from the result.
- AI that is built but not validated or deployed · Thousands of cancer AI models are published; a handful are in clinical use, and fewer have shown they help patients.
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not linked directly; found by shared links- InstitutionEMBL's European Bioinformatics Institute
Shares DrugBank & ChEMBL, Open Targets Platform.