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Opioid Use Disorder (OUD) has emerged as a significant global public healthissue, with complex multifaceted conditions. Due to the lack of effectivetreatment options for various conditions, there is a pressing need for thediscovery of new medications. In this study, we propose a deep generative modelthat combines a stochastic differential equation (SDE)-based diffusion modelingwith the latent space of a pretrained autoencoder model. The moleculargenerator enables efficient generation of molecules that are effective onmultiple targets, specifically the mu, kappa, and delta opioid receptors.Furthermore, we assess the ADMET (absorption, distribution, metabolism,excretion, and toxicity) properties of the generated molecules to identifydrug-like compounds. To enhance the pharmacokinetic properties of some leadcompounds, we employ a molecular optimization approach. We obtain a diverse setof drug-like molecules. We construct binding affinity predictors by integratingmolecular fingerprints derived from autoencoder embeddings, transformerembeddings, and topological Laplacians with advanced machine learningalgorithms. Further experimental studies are needed to evaluate thepharmacological effects of these drug-like compounds for OUD treatment. Ourmachine learning platform serves as a valuable tool in designing and optimizingeffective molecules for addressing OUD.

Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex