Analyzing the Chemical Space of Opioid Receptor Agonists and Antagonists: Insights from Computational Models
Issued Date
2026-06-09
Resource Type
eISSN
24701343
Scopus ID
2-s2.0-105041337692
Journal Title
ACS Omega
Volume
11
Issue
22
Start Page
32380
End Page
32390
Rights Holder(s)
SCOPUS
Bibliographic Citation
ACS Omega Vol.11 No.22 (2026) , 32380-32390
Suggested Citation
Yu T., Anuwongcharoen N., Wang Z.J., Piacham T. Analyzing the Chemical Space of Opioid Receptor Agonists and Antagonists: Insights from Computational Models. ACS Omega Vol.11 No.22 (2026) , 32380-32390. 32390. doi:10.1021/acsomega.5c12203 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/117339
Title
Analyzing the Chemical Space of Opioid Receptor Agonists and Antagonists: Insights from Computational Models
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
The opioid crisis has imposed a significant financial burden on the United States, costing billions of dollars annually. The recent surge in opioid overdoses has further exacerbated this crisis, placing immense strain on public health resources and the criminal justice system. Currently, all four FDA-approved medications for medication-assisted treatment (MAT) of opioid use disorder (OUD) target opioid receptors (ORs), highlighting the importance of understanding their pharmacology. This computational study investigates the chemical space of agonists and antagonists of the three primary opioid receptors─mu-opioid receptor (MOR), kappa-opioid receptor (KOR), and delta-opioid receptor (DOR)─by analyzing their physicochemical properties, Murcko scaffolds, and structure–activity relationships (SARs). Using data sets sourced from the ChEMBL database, the study focuses on IC<inf>50</inf> and EC<inf>50</inf> values, compiling a total of six data sets. To visualize the distribution of selective and nonselective ligands, the compounds were mapped within chemical space using t-SNE dimensionality reduction and embedding, employing the Klekota-Roth Count fingerprint. Within this space, k-means clustering was applied to group compounds from each data set, supporting the development of QSAR models, which demonstrated satisfactory predictive performance across most clusters. Additionally, matched molecular pair (MMP) analysis was conducted, identifying MMP cliffs, which highlight key structural modifications influencing bioactivity. These findings provide valuable insights for opioid receptor-targeted drug discovery, particularly in the optimization of opioid receptor agonists and antagonists, which could contribute to the development of safer and more effective therapeutic alternatives for pain management and opioid addiction treatment.