A Perspective introducing the "Avoid-ome" — a finite set of anti-target proteins driving drug safety liabilities — and how OpenADMET combines high-throughput structural biology, active learning, and community challenges to build mechanistically grounded predictive models.
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.
Benchmarking activity and structure prediction on a large dataset of human PXR-active compounds, with both an activity track and a structure track.
Predictive models and experimental datasets from OpenADMET blind challenges and data generation efforts.
Our quarterly newsletter detailing our progress, goals and priorities.
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.