OpenADMET runs community blind challenges to benchmark predictive models on realistic drug discovery datasets. These challenges create rigorous, transparent tests of performance while helping release valuable datasets and methods to the broader community.
Our current blind challenge focuses on human PXR induction, an important ADMET liability associated with drug-drug interactions, hepatotoxicity, and late-stage development risk. The challenge includes both an activity prediction track and a structure prediction track, built on a large OpenADMET-generated dataset designed to resemble realistic lead-optimization workflows.
A growing archive of community challenges built around realistic experimental datasets.
A lead-optimization-style blind challenge based on real-world ADMET data from Expansion Therapeutics. Participants predicted nine ADMET endpoints using earlier-stage molecules to forecast late-stage compounds.
An earlier OpenADMET-associated community blind challenge focused on pan-coronavirus drug discovery data, bringing together participants to evaluate computational methods on realistic potency and structure tasks.