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

Area of Science:

• Computational chemistry and pharmacology
• Artificial intelligence in drug discovery
• Medicinal chemistry

Background:

• Opioid use disorder (OUD) presents a critical global health challenge, demanding novel therapeutic interventions.
• Existing treatments for OUD are limited, highlighting the urgent need for innovative drug development strategies.

Purpose of the Study:

• To develop and validate a deep generative model for designing novel molecules targeting opioid receptors.
• To assess the drug-likeness and pharmacokinetic properties of generated compounds for potential OUD therapeutics.

Main Methods:

• Utilized a deep generative model combining a stochastic differential equation (SDE)-based diffusion model and a pretrained autoencoder.
• Generated molecules targeting mu, kappa, and delta opioid receptors.
• Assessed ADMET properties and employed molecular optimization for pharmacokinetic enhancement.
• Developed advanced binding affinity predictors using diverse molecular fingerprints and embeddings.

Main Results:

• Successfully generated druglike molecules with potential to target multiple opioid receptors.
• Identified lead compounds with improved pharmacokinetic profiles through molecular optimization.
• Validated the efficacy of machine learning-driven approaches in predicting binding affinities.

Conclusions:

• The developed machine learning platform offers a powerful tool for designing effective molecules to address OUD.
• Generated compounds warrant further experimental evaluation for their pharmacological effects.
• This approach accelerates the discovery of novel therapeutics for opioid use disorder.