ArticleACS chemical neuroscience2024
Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.
Article in ACS chemical neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed.
- µORScreen: a lightweight consensus modeling framework for µ-opioid receptor ligand prediction and virtual screening.RSC advances · 2026Article
- From molecular mechanisms to digital surveillance: the 2010-2025 evolution of benzodiazepine and Z-drug misuse research.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Analyzing the Chemical Space of Opioid Receptor Agonists and Antagonists: Insights from Computational Models.ACS omega · 2026Article
- μOR-ligand: target-aware view-based hybrid feature selection for μ-opioid receptor ligand functional classification.Journal of computer-aided molecular design · 2025Article
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.Experimental biology and medicine (Maywood, N.J.) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured
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Registered trials
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