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Opioid use disorder (OUD) is a chronic and relapsing condition that involvesthe continued and compulsive use of opioids despite harmful consequences. Thedevelopment of medications with improved efficacy and safety profiles for OUDtreatment is urgently needed. Drug repurposing is a promising option for drugdiscovery due to its reduced cost and expedited approval procedures.Computational approaches based on machine learning enable the rapid screeningof DrugBank compounds, identifying those with the potential to be repurposedfor OUD treatment. We collected inhibitor data for four major opioid receptorsand used advanced machine learning predictors of binding affinity that fuse thegradient boosting decision tree algorithm with two natural language processing(NLP)-based molecular fingerprints and one traditional 2D fingerprint. Usingthese predictors, we systematically analyzed the binding affinities of DrugBankcompounds on four opioid receptors. Based on our machine learning predictions,we were able to discriminate DrugBank compounds with various binding affinitythresholds and selectivities for different receptors. The prediction resultswere further analyzed for ADMET (absorption, distribution, metabolism,excretion, and toxicity), which provided guidance on repurposing DrugBankcompounds for the inhibition of selected opioid receptors. The pharmacologicaleffects of these compounds for OUD treatment need to be tested in furtherexperimental studies and clinical trials. Our machine learning studies providea valuable platform for drug discovery in the context of OUD treatment.

Machine-learning Repurposing of DrugBank Compounds for Opioid Use Disorder