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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • James S. Fraser,
  • Steven Edgar,
  • L. Naomi Handly,
  • Sriram Kosuri,
  • John D. Chodera,
  • Mark Murcko,
  • W. Patrick Walters

摘要

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.