Evidence map›Paper›PMID 39144561›Full record

ArticleACS pharmacology & translational science2024

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

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

Abstract read
In one paragraph

Article in ACS pharmacology & translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xin HuNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.ORCID https://orcid.org/0000-0003-1164-5130
Paul ShinnNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.
Zina ItkinNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.
Lin YeNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.
Ya-Qin ZhangNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.
Min ShenNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.ORCID https://orcid.org/0000-0002-8218-0433
Stephanie Ford-ScheimerNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.
Matthew D HallNational Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.ORCID https://orcid.org/0000-0002-5073-442X

Funding

Research Services Section (RSS)ZICTR000242 · NCATS · NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES · PI WILSON, KELLI · 2015 to 2025
$64.9M
6 · The paper itself

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.

Identifiers

PMID39144561
PMCPMC11320728

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.