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

Hongsong, Feng,; Elladki, Rana; Jian, Jiang; Wei, Guo-Wei

doi:10.1016/j.compbiomed.2023.106745

Cited by 10 publications

(13 citation statements)

References 53 publications

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“…This strategy can enhance machine learning predictions , and typically outperforms individual models. Such approaches were employed in our previous studies on OUD, which involved machine learning repurposing of DrugBank compounds for OUD treatment and machine learning analysis of the OUD interactome networks …”

Section: Methodsmentioning

confidence: 99%

“…Such approaches were employed in our previous studies on OUD, which involved machine learning repurposing of DrugBank compounds for OUD treatment 41 and machine learning analysis of the OUD interactome networks. 42 Both DNN and GBDT algorithm are popular algorithms in building machine learning models. DNN has advantages of dealing large and complex datasets, constructing hierarchical features and modeling complex nonlinear relationships.…”

Section: Structure Of Multitarget Stochastic Generative Network Compl...mentioning

confidence: 99%

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

Feng,

Wang,

Zhan

et al. 2023

J. Med. Chem.

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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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Section: Methodsmentioning

confidence: 99%

Section: Structure Of Multitarget Stochastic Generative Network Compl...mentioning

confidence: 99%

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

Feng,

Wang,

Zhan

et al. 2023

J. Med. Chem.

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“…Furthermore, ADMET analysis is a crucial procedure for drug discovery and repositioning. 62 Since a satisfactory candidate drug must meet ADMET criteria containing the attributes of pharmacokinetics, we utilized ADMETlab 2.0 63,64 servers for ADMET properties screening to verify the promising prediction drugs, and the related details of the optimal ranges for the properties are reported in Table S4 of Supporting Information. Specifically, the ADMETlab servers evaluate the ADMET properties of the four drugs having high predicting scores, and we focused on 13 indexes of the predictions.…”

Section: Denoised Similarity Features Of Drug and Disease Extractionmentioning

confidence: 99%

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

Ren,

Yu,

et al. 2023

J. Chem. Inf. Model.

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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.

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“…Machine learning methods have been widely used for toxicity prediction, novel drug discovery, and drug repurposing. 21–29 With the large amount of data from existing studies on the main protease binding activity, machine learning is a valuable way to identify the potential main protease binders from FDA-approved drugs.…”

Section: Introductionmentioning

confidence: 99%

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

Liu,

Xu,

Guo

et al. 2023

Exp Biol Med (Maywood)

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The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

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

Cited by 10 publications

References 53 publications

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

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

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

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

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