Open data, open models, and community blind challenges for predicting drug absorption, distribution, metabolism, excretion, and toxicity.
The CYP Inhibition Blind Challenge has wrapped up
Open data, open models, and community blind challenges for predicting drug absorption, distribution, metabolism, excretion, and toxicity.
MacDermott-Opeskin, H.; Castellanos, M.; Swain, J. A.; Scheen, J.; Colby, S.; Channing, G.; Simpkins, S.; Warneford-Thompson, R.; Correy, G. J.; Handly, L. N.; Kosuri, S.; Chodera, J. D.; Fraser, J. S.; Walters, W. P. What running OpenADMET’s blind challenges has taught us about the gap between reported model performance and using ADMET models in live drug discovery projects.
Read More →An open science effort to improve prediction of safety and toxicity for small molecules through high-quality data, mechanistic insight, and machine learning.
Predicting direct and time-dependent inhibition across four major CYP isoforms: CYP3A4, CYP2C9, CYP2D6 and CYP1A2.
Predictive models and experimental datasets from OpenADMET blind challenges and data generation efforts.
Let’s have those country roads take us to the finish line! After 2 months and more than 6,000 submissions, we finally have our winners.
Science seminars, challenge webinars, and workshop recordings from the OpenADMET community.
Meet the OMSF staff, Governing Board, and our collaborators from Octant and UCSF.
A perspective on how blind challenges can help the field honestly evaluate and advance predictive modeling in drug discovery.
Our open science commitments on data, software, and publishing, and the Code of Conduct for our community and blind challenges.