Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex

Opioid use disorder (OUD) has emerged as a significant
global public
health issue, necessitating the discovery of new medications. In this
study, we propose a deep generative model that combines a stochastic
differential equation (SDE)-based diffusion model with a pretrained
autoencoder. The molecular generator enables efficient generation
of molecules that target multiple opioid receptors, including mu,
kappa, and delta. Additionally, we assess the ADMET (absorption, distribution,
metabolism, excretion, and toxicity) properties of the generated molecules
to identify druglike compounds. We develop a molecular optimization
approach to enhance the pharmacokinetic properties of some lead compounds.
Advanced binding affinity predictors were built using molecular fingerprints,
including autoencoder embeddings, transformer embeddings, and topological
Laplacians. Our process yields druglike molecules that can be used
in highly focused experimental studies to further evaluate their pharmacological
effects. Our machine learning platform serves as a valuable tool for
designing effective molecules to address OUD.