Molecular Diversity最新文献_第7页

Network pharmacology approach to unravel the neuroprotective potential of natural products: a narrative review. 网络药理学方法揭开天然产品的神经保护潜力：叙述回顾。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-25 DOI: 10.1007/s11030-025-11198-3

Pankaj Singh, Maheshkumar Borkar, Gaurav Doshi

{"title":"Network pharmacology approach to unravel the neuroprotective potential of natural products: a narrative review.","authors":"Pankaj Singh, Maheshkumar Borkar, Gaurav Doshi","doi":"10.1007/s11030-025-11198-3","DOIUrl":"https://doi.org/10.1007/s11030-025-11198-3","url":null,"abstract":"Aging is a slow and irreversible biological process leading to decreased cell and tissue functions with higher risks of multiple age-related diseases, including neurodegenerative diseases. It is widely accepted that aging represents the leading risk factor for neurodegeneration. The pathogenesis of these diseases involves complex interactions of genetic mutations, environmental factors, oxidative stress, neuroinflammation, and mitochondrial dysfunction, which complicate treatment with traditional mono-targeted therapies. Network pharmacology can help identify potential gene or protein targets related to neurodegenerative diseases. Integrating advanced molecular profiling technologies and computer-aided drug design further enhances the potential of network pharmacology, enabling the identification of biomarkers and therapeutic targets, thus paving the way for precision medicine in neurodegenerative diseases. This review article delves into the application of network pharmacology in understanding and treating neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease, and spinal muscular atrophy. Overall, this article emphasizes the importance of addressing aging as a central factor in developing effective disease-modifying therapies, highlighting how network pharmacology can unravel the complex biological networks associated with aging and pave the way for personalized medical strategies.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143952274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpretable drug-target affinity prediction based on pre-trained models' output as embeddings and based on structure-aware cross-attention for feature fusion. 基于预训练模型作为嵌入的输出和基于结构感知交叉注意的特征融合的可解释药物靶标亲和力预测。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-25 DOI: 10.1007/s11030-025-11194-7

Fang Zheng, Juanjuan Zhao, Zihang Yuan, Yuanchen Gao, Yafeng Li, Yaheng Li, Yan Geng, Yan Qiang

{"title":"Interpretable drug-target affinity prediction based on pre-trained models' output as embeddings and based on structure-aware cross-attention for feature fusion.","authors":"Fang Zheng, Juanjuan Zhao, Zihang Yuan, Yuanchen Gao, Yafeng Li, Yaheng Li, Yan Geng, Yan Qiang","doi":"10.1007/s11030-025-11194-7","DOIUrl":"https://doi.org/10.1007/s11030-025-11194-7","url":null,"abstract":"The characteristics of protein pockets can better capture the interaction information between proteins and small molecules, thereby improving the performance of drug-target interaction (DTI) prediction tasks. However, pocket data typically need to be predicted using software such as AlphaFold, which would entail a massive workload for datasets ranging from tens of thousands to hundreds of thousands of samples. Moreover, feature representation networks for 3D pocket data are computationally intensive. To address this, we propose simulating 3D pocket data using sequence data through feature fusion of two different objects based on structure cross-attention (CASD). Additionally, precise feature representation is a prerequisite for accurately identifying pocket information. We introduce a method that leverages the output of the last layer of a pre-trained model as an embedding layer for training a new model from scratch. This approach not only incorporates prior knowledge from the pre-trained model but also expands model capacity, enabling more accurate feature representation. Furthermore, we enhance the multimodal representation of small molecule compounds using feature fusion based on structure cross-attention for the same object (CASS), further improving feature representation capabilities. Our cross-attention mechanisms operate at the token-level or node-level, allowing fine-grained capture of interactions between amino acids and atoms. This enables the identification of the contribution score of each atom or amino acid to the task, making our model interpretable for drug-target prediction. Experimental validation demonstrates that our model achieves state-of-the-art predictive performance.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling. 二氧化钛：一个集成的工具，用于硅分子性质预测和基于纳米结构的建模。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-23 DOI: 10.1007/s11030-025-11196-5

Nikoletta-Maria Koutroumpa, Maria Antoniou, Dimitra-Danai Varsou, Konstantinos D Papavasileiou, Nikolaos K Sidiropoulos, Christoforos Kyprianou, Andreas Tsoumanis, Haralambos Sarimveis, Iseult Lynch, Georgia Melagraki, Antreas Afantitis

{"title":"Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling.","authors":"Nikoletta-Maria Koutroumpa, Maria Antoniou, Dimitra-Danai Varsou, Konstantinos D Papavasileiou, Nikolaos K Sidiropoulos, Christoforos Kyprianou, Andreas Tsoumanis, Haralambos Sarimveis, Iseult Lynch, Georgia Melagraki, Antreas Afantitis","doi":"10.1007/s11030-025-11196-5","DOIUrl":"https://doi.org/10.1007/s11030-025-11196-5","url":null,"abstract":"Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular properties is crucial for rational compound design in the chemical industry. To address this need, we have developed predictive models for nine key properties, including the octanol/water partition coefficient, water solubility, experimental hydration free energy in water, vapor pressure, boiling point, cytotoxicity, mutagenicity, blood-brain barrier permeability, and bioconcentration factor. These models have demonstrated high predictive accuracy and have undergone thorough validation in accordance with OECD test guidelines. The models are seamlessly integrated into the Enalos Cloud Platform through Titania ( https://enaloscloud.novamechanics.com/EnalosWebApps/titania/ ), a comprehensive web-based application designed to democratize access to advanced computational tools. Titania features an intuitive, user-friendly interface, allowing researchers, regardless of computational expertise, to easily employ models for property prediction of novel compounds. The platform enables informed decision-making and supports innovation in drug discovery and material design. We aspire for this tool to become a valuable resource for the scientific community, enhancing both the efficiency and accuracy of property and toxicity predictions.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity. 推进抗疟药物的发现：预测PfPK6抑制剂活性的集成机器学习模型。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-22 DOI: 10.1007/s11030-025-11203-9

Maryam Gholami, Mohammad Asadollahi-Baboli

{"title":"Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.","authors":"Maryam Gholami, Mohammad Asadollahi-Baboli","doi":"10.1007/s11030-025-11203-9","DOIUrl":"https://doi.org/10.1007/s11030-025-11203-9","url":null,"abstract":"Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein kinase 6 (PfPK6) inhibitors, employing a range of machine learning techniques to develop ensemble regression and classification models. Molecular descriptors were refined using classification and regression trees (CART) to identify the most relevant features. Six machine learning algorithms (Random Forest (RF), Relevance Vector Machine (RVM), Support Vector Machine (SVM), Cubist, Artificial Neural Networks (ANN), and XGBoost) were utilized to construct regression models. The consensus model demonstrated superior predictive performance, achieving R2Test = 0.94, SETest = 0.20, Q2CV = 0.90, and SECV = 0.25, outperforming individual models. For classification tasks, five algorithms were evaluated and a majority voting approach yielded an accuracy of 91% and a sensitivity of 93%. The robustness of the models was confirmed through applicability domain analysis (96% coverage) and y-randomization tests, ensuring that the predictive outcomes were not due to chance correlations. This study highlights the effectiveness of ensemble machine learning approaches in predictive modeling and provides critical insights for the rational design of novel PfPK6 inhibitors.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rational design of structurally rigidified ketone-peptide deformylase inhibitors with enhanced membrane permeability for combating gram-negative bacterial infections. 抗革兰氏阴性细菌感染的结构刚性酮肽去甲酰基酶抑制剂的合理设计。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-22 DOI: 10.1007/s11030-025-11193-8

Zhonghui Zhang, Jidi Hu, Maoqing Shi, Xiaoxiao Gong, Taoda Shi, Hongxia Li, Yu Qian, Wenhao Hu

{"title":"Rational design of structurally rigidified ketone-peptide deformylase inhibitors with enhanced membrane permeability for combating gram-negative bacterial infections.","authors":"Zhonghui Zhang, Jidi Hu, Maoqing Shi, Xiaoxiao Gong, Taoda Shi, Hongxia Li, Yu Qian, Wenhao Hu","doi":"10.1007/s11030-025-11193-8","DOIUrl":"https://doi.org/10.1007/s11030-025-11193-8","url":null,"abstract":"Gram-negative bacterial infections remain a critical global health challenge due to their complex membrane structure and limited treatment options. While peptide deformylase (PDF) inhibitors demonstrate potent activity against Gram-positive pathogens, their efficacy against Gram-negative species is constrained by poor outer membrane permeability. To address this, we rationally designed a novel series of ketone-incorporated compounds with enhanced structural rigidity to improve membrane penetration. Our lead compounds (10a, 10f, 12b) exhibited exceptional activity against Acinetobacter baumannii (MIC50 < 2 μg/mL) and clinically isolated strains (MIC50 < 8 μg/mL), with compound 6 showing particularly potent PDF inhibition (IC50 = 70.8 ± 8.0 nM). The lead compound demonstrated no significant cytotoxicity toward human hepatic stellate cells (LX-2) at the tested concentrations. Molecular docking confirmed their mechanism of action through competitive PDF binding. This work establishes a strategic framework for developing next-generation antibiotics against Gram-negative infections by optimizing membrane permeability while maintaining target inhibition.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benzoxazolone-based FITC-conjugated fluorescent probe for locating in-vivo expression level of translocator protein (TSPO) during lung inflammation. 基于苯并恶唑酮的fitc共轭荧光探针用于定位肺部炎症中转运蛋白（TSPO）的体内表达水平。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-21 DOI: 10.1007/s11030-025-11192-9

Neelam Kumari, Anupriya Adhikari, Sunita Bhagat, Anil K Mishra, Anjani K Tiwari

{"title":"Benzoxazolone-based FITC-conjugated fluorescent probe for locating in-vivo expression level of translocator protein (TSPO) during lung inflammation.","authors":"Neelam Kumari, Anupriya Adhikari, Sunita Bhagat, Anil K Mishra, Anjani K Tiwari","doi":"10.1007/s11030-025-11192-9","DOIUrl":"https://doi.org/10.1007/s11030-025-11192-9","url":null,"abstract":"Translocator protein 18 kDa (TSPO) has been a salient target for probing and monitoring inflammation in the central nervous system (CNS) and peripheral systems. Leveraging our previously developed, TSPO specific, modified acetamidobenzoxazolone derivative, the present work describes the synthesis and development of an optical probe for lung inflammation imaging: 2-(3,6-dihydroxy-9H-xanthen-9-yl)-5-(3-(3-(2-(methyl(phenyl)amino)-2-oxoethyl)-2-oxo-2,3-dihydrobenzo[d]oxazol-5-yl)thioureido)benzoic acid (FITC-MBP). The FITC-MBP is prepared through facile methodology by conjugating MBP to fluorophore dye FITC. Spectral properties remained equivalent to FITC dye with absorption and emission wavelength at 486 and 520 nm, respectively. Cellular uptake studies established overexpression of TSPO in lipopolysaccharide (LPS)-induced inflammation in H1299 lung cells. Reduced mean fluorescence intensity (MFI) during blocking experiments with PK11195 in flow cytometry suggests the specificity of the fluorescent probe towards TSPO. In-vivo optical imaging analysis on LPS-induced lung-inflamed balb/c mice revealed major sequestration of FITC-MBP in the lungs compared to control at 25 min post-injection that significantly decreased on pretreatment with PK11195 due to competitive binding to TSPO. On ground of these findings, we believe the novel fluorescent probe (FITC-MBP) might be utilized to visualize the overexpressed TSPO.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PPI networking, in-vitro expression analysis, virtual screening, DFT, and molecular dynamics for identifying natural TNF-α inhibitors for rheumatoid arthritis. PPI网络、体外表达分析、虚拟筛选、DFT和分子动力学鉴定类风湿性关节炎天然TNF-α抑制剂

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-19 DOI: 10.1007/s11030-025-11158-x

Yogaswaran Velmurugan, Nandhini Chakkarapani, Sathan Raj Natarajan, Selvaraj Jayaraman, Hemamalini Madhukar, Rajakannan Venkatachalam

{"title":"PPI networking, in-vitro expression analysis, virtual screening, DFT, and molecular dynamics for identifying natural TNF-α inhibitors for rheumatoid arthritis.","authors":"Yogaswaran Velmurugan, Nandhini Chakkarapani, Sathan Raj Natarajan, Selvaraj Jayaraman, Hemamalini Madhukar, Rajakannan Venkatachalam","doi":"10.1007/s11030-025-11158-x","DOIUrl":"https://doi.org/10.1007/s11030-025-11158-x","url":null,"abstract":"In humans, rheumatoid arthritis (RA) is a deadly autoimmune disease that affects bone health. Although the specific etiology of RA is unknown, scientific evidence suggests that smoking, genetic abnormalities, and environmental factors may all contribute to the disease's progression. We employed protein-protein interaction (PPI) networking analysis to identify a possible therapeutic target for RA. The lead-like molecule for the selected target was then found via virtual screening in the Indian medicinal plants phytochemistry and therapeutics database. Molecular dynamics has confirmed the stability of drug target-lead-like molecule complexes. The networking analysis identifies TNF-α as a potential therapeutic target for RA. TNF-α expression was verified using in vitro studies. Cassamedine was identified as a possible lead molecule among 17,967 chemicals in the Indian Medicinal Plants Phytochemistry and Therapeutics database using virtual screening experiments. The molecular docking results of the lead compound interaction with TNF-α were clarified by the quantum mechanism (QM) technique, namely, density functional theory (DFT). The stability of the lead-like compound with TNF-α was confirmed using 200 ns of molecular dynamics simulations. Energy calculations using molecular mechanics Poisson-Boltzmann surface area (MMPBSA) confirm the free energy between TNF-α and lead-like molecules.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction. QMGBP-DL：量子分子图带隙预测的深度学习和机器学习方法。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-19 DOI: 10.1007/s11030-025-11178-7

Outhman Abbassi, Soumia Ziti

{"title":"QMGBP-DL: a deep learning and machine learning approach for quantum molecular graph band-gap prediction.","authors":"Outhman Abbassi, Soumia Ziti","doi":"10.1007/s11030-025-11178-7","DOIUrl":"https://doi.org/10.1007/s11030-025-11178-7","url":null,"abstract":"Predicting molecular and quantum material properties, especially the band gap, is crucial for accelerating discoveries in drug design and material science. Although graph neural networks and probabilistic encoders are well established in molecular data analysis, their targeted integration and application for band-gap prediction remain an active research area. This paper introduces QMGBP-DL, a deep learning approach that combines a molecular graph encoder with machine learning models to improve the prediction accuracy of molecular and material band-gap energy. The encoder uses graph convolutional networks to derive latent representations of chemical structures from SMILES strings, optimized via Kullback-Leibler divergence loss. These representations serve as inputs for training various machine learning models to predict properties. QMGBP-DL's effectiveness is assessed using the QM9, PCQM4M, and OPV datasets, demonstrating significant improvements, particularly with a random forest model for property prediction. A comparative analysis against established approaches DenseGNN, MEGNet, and ALIGNN reveals that QMGBP-DL excels in predicting HOMO, LUMO, and band gap, achieving notably lower MAE values. The integration of GCN-derived latent spaces with traditional machine learning models, especially Random Forest, provides a powerful approach for band-gap prediction. The results highlight the efficacy of our integrated approach, showcasing that graph-based molecular encoding combined with machine learning, particularly Random Forest, is highly effective for accurate band-gap prediction, thereby facilitating material discovery and design.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143954943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computer-aided ligand identification of capsaicinoids and their potential functions in metabolic diseases. 辣椒素的计算机辅助配体鉴定及其在代谢疾病中的潜在功能。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-19 DOI: 10.1007/s11030-025-11182-x

Ana Alondra Sobrevilla-Navarro, Omar Ramos-Lopez, Bertha Landeros-Sánchez, María Guadalupe Sánchez-Parada, Ana Elizabeth González-Santiago

{"title":"Computer-aided ligand identification of capsaicinoids and their potential functions in metabolic diseases.","authors":"Ana Alondra Sobrevilla-Navarro, Omar Ramos-Lopez, Bertha Landeros-Sánchez, María Guadalupe Sánchez-Parada, Ana Elizabeth González-Santiago","doi":"10.1007/s11030-025-11182-x","DOIUrl":"https://doi.org/10.1007/s11030-025-11182-x","url":null,"abstract":"Obesity, diabetes, and cardiovascular diseases are major health concerns worldwide. In recent times, research has focused on identifying food-derived molecules and their relationship with metabolic diseases. A study was conducted to establish a process for characterizing the biological targets of capsaicinoids found in chili peppers. Capsaicinoids are a group of compounds including Capsaicin, Dihydrocapsaicin, Nordihydrocapsaicin, Homodihydrocapsaicin, Homocapsaicin, and Nonivamide. The study aimed to use bioinformatics tools to analyze these compounds and their effect on metabolic targets. To achieve this, a search was conducted for SMILES sequences of chili pepper capsaicinoids. The 2D and 3D similarity analyses were performed with compounds known to be experimentally active on their protein targets. These ligands were then analyzed, and predictions were made about enriched biological terms and bio-pathways. A protein-protein interaction analysis was performed on metabolic targets. Additionally, pharmacokinetics and CYP450 interaction prediction were analyzed using capsaicinoids. The molecular activity of the identified ligands for the six capsaicinoids were classified as G-protein-coupled receptors, proteases, membrane receptors, oxidoreductases, erasers, electrochemical transporters, cytochrome P450s, and hydrolases. There are several signaling pathways modulated by capsaicinoids, including insulin signaling, insulin resistance, AGE-RAGE signaling in diabetic complications, endocrine resistance, lipid metabolism, and atherosclerosis. The study found that capsaicin interacts more strongly with pathways that are important in metabolic diseases, such as obesity, cancer, diabetes mellitus, and their complications. These findings could be useful in developing strategies to mitigate the impact of metabolic diseases.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gut microbial metabolites targeting JUN in renal cell carcinoma via IL-17 signaling pathway: network pharmacology approach. 通过IL-17信号通路靶向肾癌JUN的肠道微生物代谢物：网络药理学方法

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-04-18 DOI: 10.1007/s11030-025-11188-5

Stany Bala Kumar, Shatakshi Mishra, Anushka Das, Sagnik Nag, Rakesh Naidu

{"title":"Gut microbial metabolites targeting JUN in renal cell carcinoma via IL-17 signaling pathway: network pharmacology approach.","authors":"Stany Bala Kumar, Shatakshi Mishra, Anushka Das, Sagnik Nag, Rakesh Naidu","doi":"10.1007/s11030-025-11188-5","DOIUrl":"https://doi.org/10.1007/s11030-025-11188-5","url":null,"abstract":"The gut microbiome plays a crucial role in renal diseases, influencing conditions such as renal cell carcinoma (RCC), acute kidney injuries, and diabetic nephropathy. Recent studies highlight the association between gut microbial metabolites (GMM) and RCC progression. This study employs a computational network pharmacology framework to explore the mechanistic action of gut microbiota-derived metabolites against RCC. GMM were selected from the gutMgene database and analyzed for common targets using DisGeNET, Gene Card, and OMIM. Downstream analysis included gene ontology, KEGG pathway enrichment, metabolite-target-pathway-disease network construction, and protein-protein interaction analysis. Further, key metabolites were evaluated for drug-likeness, ADMET properties, and molecular docking, followed by molecular dynamics simulations (MDS) to assess complex stability. The JUN/AP-1 gene emerged as the prime target, exhibiting the highest binding affinity with Icaritin (- 5.9 kcal/mol), followed by Quercetin and Luteolin. MDS confirmed the stable binding of Icaritin to the active site throughout the simulation. These GMM may influence anticancer activity through distinct regulatory pathways involving the JUN/AP-1 gene, either by inhibiting or modulating its function. These insights establish a basis for further in vitro and in vivo investigations, supporting the development of microbiome-based therapeutic approaches.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0