Mapping the avoid-ome: a systematic open-science approach to predictive ADMET
James S Fraser1, Steven Edgar2, L Naomi Handly2
1Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, USA and Quantitative Biosciences Institute, University of California San Francisco, San Francisco, California, USA. jfraser@fraserlab.com.
Nature Communications
|May 25, 2026
Summary
Drug discovery failures due to ADMET issues are reduced by OpenADMET. This initiative uses structural biology and community efforts to create datasets for rational drug design targeting "anti-targets".
Area of Science:
- Drug discovery and development
- Computational chemistry
- Structural biology
Background:
- Drug discovery frequently encounters setbacks (30% of clinical failures) due to unpredictable Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties.
- Current methods lack the detailed, atomistic insights required to address the
- The
- Avoid-ome
- - a set of proteins acting as
- anti-targets
- - presents a significant challenge in rational drug design.
Purpose of the Study:
- To establish OpenADMET, an open-science initiative focused on generating pre-competitive, mechanistic datasets.
- To address the limitations of conventional drug discovery methods by providing atomistic detail on
- anti-targets
- .
Main Methods:
- Utilizing high-throughput structural biology techniques.
- Employing active learning strategies to refine models.
- Leveraging community challenges to foster collaboration and data generation.
Main Results:
- Development of generalizable predictive models grounded in structural
- ground truth
- .
Conclusions:
- OpenADMET directly studies the
- Avoid-ome
- to overcome ADMET-related failures.
- Facilitating a new era of rational, multi-parameter drug design through open science and mechanistic datasets.
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