ArticleInternational journal of molecular sciences2019
Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Analyzing the Chemical Space of Opioid Receptor Agonists and Antagonists: Insights from Computational Models.ACS omega · 2026Article
- Quantum-topological simulation of Berry phase-induced fentanyl-μ-opioid receptor dissociation via terahertz vortex fields.Journal of molecular modeling · 2025Article
- Focus on Cognitive Enhancement: A Narrative Overview of Nootropics and "Smart Drug" Use and Misuse.Biology · 2025Review
- Acute Effects of the Psychedelic Phenethylamine 25I-NBOMe in C57BL/6J Male Mice.International journal of molecular sciences · 2025Article
- Xylazine as an emerging new psychoactive substance; focuses on both 5-HTArchiv der Pharmazie · 2025Article
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- QSAR-based physiologically based pharmacokinetic (PBPK) modeling for 34 fentanyl analogs: model validation, human pharmacokinetic prediction and abuse risk insights.Frontiers in pharmacology · 2025Article
- Steered Molecular Dynamics Simulations Study on FABP4 Inhibitors.Molecules (Basel, Switzerland) · 2023Article
- Heparan Sulfate and Enoxaparin Interact at the Interface of the Spike Protein of HCoV-229E but Not with HCoV-OC43.Viruses · 2023Article
- Targeting the SARS-CoV-2 HR1 with Small Molecules as Inhibitors of the Fusion Process.International journal of molecular sciences · 2022Article
- The Psychonauts' Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors.Journal of chemical information and modeling · 2021Article
- Machine learningRSC advances · 2021Article
- Construction of a Virtual Opioid Bioprofile: A Data-Driven QSAR Modeling Study to Identify New Analgesic Opioids.ACS sustainable chemistry & engineering · 2021Article
- Computational Tools in the Discovery of FABP4 Ligands: A Statistical and Molecular Modeling Approach.Marine drugs · 2019Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (OR) ligands have been developed. The resulted models, exploiting the accessibility of the QSAR modeling, generate a useful tool for the investigation and identification of unclassified fentanyl-like structures. The models have been built using a set of 115 molecules using Forge as a software, and the quality was confirmed by statistical analysis, resulting in being effective for their predictive and descriptive capabilities. The three different approaches were then combined to produce a consensus model and were exploited to explore the chemical landscape of 3000 fentanyl-like structures, generated by a theoretical scaffold-hopping approach. The findings of this study should facilitate the identification and classification of new OR ligands with fentanyl-like structures.
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