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.

阿片类使用障碍（Opioid use disorder, OUD）已成为重大的全球性公共卫生议题，亟待开发新型治疗药物。本研究提出一种深度生成模型，将基于随机微分方程（stochastic differential equation, SDE）的扩散模型与预训练自编码器相结合。该分子生成模块可高效生成针对μ、κ、δ三种阿片受体的靶向分子。此外，我们对生成分子的ADMET（吸收、分布、代谢、排泄与毒性）性质进行评估，以筛选类药化合物。我们开发了一种分子优化方法，以优化部分先导化合物的药代动力学特性。本研究基于分子指纹（包括自编码器嵌入、Transformer嵌入与拓扑拉普拉斯算子）构建了高精度结合亲和力预测模型。本研究流程所得到的类药分子可用于针对性实验研究，进一步评估其药理学效应；我们的机器学习平台可为开发针对阿片类使用障碍的有效分子提供有力工具。