Loading
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.
Shares DrugBank & ChEMBL, Open Targets Platform.
Shares The Repurposing Drugs in Oncology (ReDO) Project, No incentive to repurpose cheap drugs.
Shares The Repurposing Drugs in Oncology (ReDO) Project, No incentive to repurpose cheap drugs.
Shares The Repurposing Drugs in Oncology (ReDO) Project, No incentive to repurpose cheap drugs.
Shares The Repurposing Drugs in Oncology (ReDO) Project, No incentive to repurpose cheap drugs.
Shares The Repurposing Drugs in Oncology (ReDO) Project, No incentive to repurpose cheap drugs.
Shares The Repurposing Drugs in Oncology (ReDO) Project, No incentive to repurpose cheap drugs.
Shares The Repurposing Drugs in Oncology (ReDO) Project, No incentive to repurpose cheap drugs.