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用于高通量筛选和人工智能驱动药物发现的疼痛与阿片类物质使用障碍化合物综合集。

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

作者信息

Hu Xin, Shinn Paul, Itkin Zina, Ye Lin, Zhang Ya-Qin, Shen Min, Ford-Scheimer Stephanie, Hall Matthew D

机构信息

National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.

出版信息

ACS Pharmacol Transl Sci. 2024 Jul 22;7(8):2391-2400. doi: 10.1021/acsptsci.4c00256. eCollection 2024 Aug 9.

Abstract

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.

摘要

作为美国国立卫生研究院(NIH)长期助力终结成瘾(HEAL)计划的一部分,国立推进转化科学中心致力于开发用于管理和治疗疼痛以及预防和治疗阿片类药物滥用与成瘾的新型药理学工具和研究性药物。为实现这些目标,我们创建了一个全面的、带有注释的小分子文库,其中包括作用于已发表的与疼痛和成瘾相关靶点的药物、探针和工具化合物。近3000种与约200个已知和假设的HEAL靶点相关的小分子已被汇集、整理并注释于一个集合中。文库化合物的实物样本已获取并以1536孔板形式铺板,可针对广泛的检测方法进行快速高效的高通量筛选。HEAL靶点与化合物文库的创建,再加上用于人工智能驱动的机器学习、结构建模和虚拟筛选的集成计算平台,为与疼痛和阿片类药物使用障碍(OUD)相关的新型靶点的战略药物重新利用、创新分析和假设检验提供了宝贵资源。该文库可供研究人员用于筛选与疼痛和OUD相关的表型。

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