A Comprehensive Collection of Pain and Opioid Use Disorder Compounds for High-Throughput Screening and Artificial Intelligence-Driven Drug Discovery

药物发现 高通量筛选 吞吐量 阿片类药物使用障碍 药品 类阿片 计算机科学 人工智能 医学 药理学 生物信息学 生物 内科学 电信 受体 无线
作者
Xin Hu,Paul Shinn,Zina Itkin,Lin Ye,Ya-Qin Zhang,Min Shen,Stephanie L. Ford‐Scheimer,Matthew D. Hall
出处
期刊:ACS pharmacology & translational science [American Chemical Society]
卷期号:7 (8): 2391-2400 被引量:2
标识
DOI:10.1021/acsptsci.4c00256
摘要

As part of the NIH Helping to End Addiction Long-term (HEAL) Initiative, the National Center for Advancing Translational Sciences is dedicated to the development of new pharmacological tools and investigational drugs for managing and treating pain as well as the prevention and treatment of opioid misuse and addiction. In line with these objectives, we created a comprehensive, annotated small molecule library including drugs, probes, and tool compounds that act on published pain- and addiction-relevant targets. Nearly 3000 small molecules associated with approximately 200 known and hypothesized HEAL targets have been assembled, curated, and annotated in one collection. Physical samples of the library compounds have been acquired and plated in 1536-well format, enabling a rapid and efficient high-throughput screen against a wide range of assays. The creation of the HEAL Targets and Compounds Library, coupled with an integrated computational platform for AI-driven machine learning, structural modeling, and virtual screening, provides a valuable source for strategic drug repurposing, innovative profiling, and hypothesis testing of novel targets related to pain and opioid use disorder (OUD). The library is available to investigators for screening pain and OUD-relevant phenotypes.

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