ArticleJournal of medicinal chemistry2023
Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.
Article in Journal of medicinal chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Computational Approach to Select Lead-Like Compounds From an Opioid Class of Novel Psychoactive Substances.Chemistry & biodiversity · 2026Article
- Auxiliary Discrminator Sequence Generative Adversarial Networks for Few Sample Molecule Generation.Journal of chemical information and modeling · 2025Article
- Artificial intelligence approaches for anti-addiction drug discovery.Digital discovery · 2025Review
- Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.Journal of chemical information and modeling · 2025Article
- A structure-based framework for selective inhibitor design and optimization.Communications biology · 2025Article
- Multi-objective latent space optimization of generative molecular design models.Patterns (New York, N.Y.) · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.