Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex
This work addresses the pressing need for effective treatments for Opioid Use Disorder, though it appears incremental as it combines existing methods like diffusion modeling and autoencoders for molecular optimization.
The study tackled the problem of discovering new medications for Opioid Use Disorder by developing a deep generative model that efficiently generates molecules effective on multiple opioid receptors and assesses their ADMET properties, resulting in a diverse set of drug-like molecules.
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.