Mapping the avoid-ome: a systematic open-science approach to predictive ADMET

Nature Communications 2026 Open access

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Authors: James S. Fraser, Steven Edgar, L. Naomi Handly, Sriram Kosuri, John D. Chodera, Mark Murcko, W. Patrick Walters
DOI: 10.1038/s41467-026-73410-8
Cited by: 4
References: 44

Abstract

Drug discovery often fails due to unpredictable ADMET issues, which account for 30% of clinical setbacks. Conventional methods lack the atomistic detail needed to navigate the "Avoid-ome"-a finite set of proteins acting as "anti-targets". OpenADMET is an open-science initiative addressing this by creating pre-competitive, mechanistic datasets. Using high-throughput structural biology, active learning, and community challenges, it builds generalizable models grounded in structural "ground truth". By directly studying the Avoid-ome, OpenADMET facilitates an era of rational, multi-parameter drug design.

Fields of Study & Subjects

Computational Drug Discovery Methods Protein Degradation and Inhibitors vaccines and immunoinformatics approaches

Publication details

Journal / source
Nature Communications
Publisher
Springer Science and Business Media LLC
Volume
17
Issue
1
Publication date
25 May 2026
Language
EN