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.
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.
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