Machine-learning analysis of opioid use disorder informed by MOR, DOR, KOR, NOR and ZOR-based interactome networks

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here, we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILES strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured Emax values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5 ± 3.9% and 91.8 ± 4.4%, respectively. To overcome the challenge of a small data set, a student-teacher learning method called tritraining with disagreement was tested using an unlabeled data set comprised of 15,816 ligands of human, mouse, and rat μOR, κOR, and δOR. We found that the tritraining scheme was able to increase the hold-out AUC of MPNN models to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor

Opioids are small-molecule agonists of μ-opioid receptor (μOR), while reversal agents such as naloxone are antagonists of μOR. Here we developed machine learning (ML) models to classify the intrinsic activities of ligands at the human μOR based on the SMILE strings and two-dimensional molecular descriptors. We first manually curated a database of 983 small molecules with measured E max values at the human μOR. Analysis of the chemical space allowed identification of dominant scaffolds and structurally similar agonists and antagonists. Decision tree models and directed message passing neural networks (MPNNs) were then trained to classify agonistic and antagonistic ligands. The hold-out test AUCs (areas under the receiver operator curves) of the extra-tree (ET) and MPNN models are 91.5±3.9% and 91.8± 4.4%, respectively. To overcome the challenge of small dataset, a student-teacher learning method called tri-training with disagreement was tested using an unlabeled dataset comprised of 15,816 ligands of human, mouse, or rat μOR, κOR, or δOR. We found that the tri-training scheme was able to increase the hold-out AUC of MPNN to as high as 95.7%. Our work demonstrates the feasibility of developing ML models to accurately predict the intrinsic activities of μOR ligands, even with limited data. We envisage potential applications of these models in evaluating uncharacterized substances for public safety risks and discovering new therapeutic agents to counteract opioid overdoses.

Nalmefene Hydrochloride: Potential Implications for Treating Alcohol and Opioid Use Disorder

Nalmefene hydrochloride was first discovered as an opioid antagonist derivative of naltrexone in 1975. It is among the most potent opioid antagonists currently on the market and is differentiated from naloxone and naltrexone by its partial agonist activity at the kappa-opioid receptor which may benefit in the treatment of alcohol use disorder. Oral nalmefene has been approved in the European Union for treatment of alcohol use disorder since 2013. As of 2023, nalmefene is available in the United States as an intranasal spray for reversal of opioid overdose but is not approved for alcohol or opioid use disorder as a maintenance treatment. The substantially longer half-life of nalmefene and 5-fold higher binding affinity to opioid receptors makes it a superior agent over naloxone in the reversal of high potency synthetic opioids like fentanyl and the emerging nitazenes. Nalmefene presents with a comparable side effect profile to other opioid antagonists and should be considered for further development as a maintenance treatment for opioid and other substance use disorders.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

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.

Involvement of the Opioid Peptide Family in Cancer Progression

Peptides mediate cancer progression favoring the mitogenesis, migration, and invasion of tumor cells, promoting metastasis and anti-apoptotic mechanisms, and facilitating angiogenesis/lymphangiogenesis. Tumor cells overexpress peptide receptors, crucial targets for developing specific treatments against cancer cells using peptide receptor antagonists and promoting apoptosis in tumor cells. Opioids exert an antitumoral effect, whereas others promote tumor growth and metastasis. This review updates the findings regarding the involvement of opioid peptides (enkephalins, endorphins, and dynorphins) in cancer development. Anticancer therapeutic strategies targeting the opioid peptidergic system and the main research lines to be developed regarding the topic reviewed are suggested. There is much to investigate about opioid peptides and cancer: basic information is scarce, incomplete, or absent in many tumors. This knowledge is crucial since promising anticancer strategies could be developed alone or in combination therapies with chemotherapy/radiotherapy.