ArticleComputers in biology and medicine2023
Machine-learning repurposing of DrugBank compounds for opioid use disorder.
Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-Analysis and Topological Perturbation in Interactomic Network for Antiopioid Addiction Drug Repurposing.Journal of chemical information and modeling · 2025Pooled it
- From molecular mechanisms to digital surveillance: the 2010-2025 evolution of benzodiazepine and Z-drug misuse research.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Novel molecular design via a scaffold-aware transformer with multi-scale attention mechanisms.Journal of cheminformatics · 2026Article
- Computational Approach to Select Lead-Like Compounds From an Opioid Class of Novel Psychoactive Substances.Chemistry & biodiversity · 2026Article
- Identifying compounds to treat opiate use disorder by leveraging multi-omic data integration and multiple drug repurposing databases.Translational psychiatry · 2025Article
- Functional brain network identification in opioid use disorder using machine learning analysis of resting-state fMRI BOLD signals.Computers in biology and medicine · 2025Article
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- Using the Coefficient of Conformism of a Correlative Prediction in Simulation of Cardiotoxicity.Toxics · 2025Article
- Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.Journal of chemical information and modeling · 2025Article
- Machine learning models to predict ligand binding affinity for the orexin 1 receptor.Artificial intelligence chemistry · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
- Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design.Communications in information and systems · 2024Article
- A critical assessment of bioactive compounds databases.Future medicinal chemistry · 2024Review
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.Journal of medicinal chemistry · 2023Article
- Pharmacophore-Based Machine Learning Model To Predict Ligand Selectivity for E3 Ligase Binders.ACS omega · 2023Article
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3 authors.
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Abstract
Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery due to its reduced cost and expedited approval procedures. Computational approaches based on machine learning enable the rapid screening of DrugBank compounds, identifying those with the potential to be repurposed for OUD treatment. We collected inhibitor data for four major opioid receptors and used advanced machine learning predictors of binding affinity that fuse the gradient boosting decision tree algorithm with two natural language processing (NLP)-based molecular fingerprints and one traditional 2D fingerprint. Using these predictors, we systematically analyzed the binding affinities of DrugBank compounds on four opioid receptors. Based on our machine learning predictions, we were able to discriminate DrugBank compounds with various binding affinity thresholds and selectivities for different receptors. The prediction results were further analyzed for ADMET (absorption, distribution, metabolism, excretion, and toxicity), which provided guidance on repurposing DrugBank compounds for the inhibition of selected opioid receptors. The pharmacological effects of these compounds for OUD treatment need to be tested in further experimental studies and clinical trials. Our machine learning studies provide a valuable platform for drug discovery in the context of OUD treatment.
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Registered trials
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 OpenQuestion graph.