Multi-objective Molecular Optimization for Opioid Use Disorder Treatment
  Using Generative Network Complex
Hongsong Feng Rui Wang Chang-Guo Zhan Guo-Wei Wei
Opioid Use Disorder (OUD) has emerged as a significant global public health
issue, with complex multifaceted conditions. Due to the lack of effective
treatment options for various conditions, there is a pressing need for the
discovery of new medications. In this study, we propose a deep generative model
that combines a stochastic differential equation (SDE)-based diffusion modeling
with the latent space of a pretrained autoencoder model. The molecular
generator enables efficient generation of molecules that are effective on
multiple 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 identify
drug-like compounds. To enhance the pharmacokinetic properties of some lead
compounds, we employ a molecular optimization approach. We obtain a diverse set
of drug-like molecules. We construct binding affinity predictors by integrating
molecular fingerprints derived from autoencoder embeddings, transformer
embeddings, and topological Laplacians with advanced machine learning
algorithms. Further experimental studies are needed to evaluate the
pharmacological effects of these drug-like compounds for OUD treatment. Our
machine learning platform serves as a valuable tool in designing and optimizing
effective molecules for addressing OUD.