Molecular Diversity最新文献_第9页

Recent progress in the development of HSP90 inhibitors: structure-activity relationship and biological evaluation studies. HSP90抑制剂的研究进展：构效关系及生物学评价研究。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-08-06 DOI: 10.1007/s11030-025-11314-3

Ankush Kumar, Aman Rai, Naresh Kumar Rangra, Thakur Gurjeet Singh, Samson Chibuzo Ugwuagbo, Rohit Bhatia

{"title":"Recent progress in the development of HSP90 inhibitors: structure-activity relationship and biological evaluation studies.","authors":"Ankush Kumar, Aman Rai, Naresh Kumar Rangra, Thakur Gurjeet Singh, Samson Chibuzo Ugwuagbo, Rohit Bhatia","doi":"10.1007/s11030-025-11314-3","DOIUrl":"10.1007/s11030-025-11314-3","url":null,"abstract":"In cellular biology, Heat Shock Protein 90 (Hsp90) plays a pivotal role in coordinating several processes essential or cellular survival, cell signaling and other processes. This analysis explores the complex structure, biological significance, and regulatory mechanisms clarifying its essential function in preserving cellular homeostasis and its relevance in a range of illnesses, such as cancer, neurological conditions, infectious diseases, cardiovascular conditions, and autoimmune diseases. Hsp90 is a prominent target for cancer treatment. The current manuscript involved the recent progress in the synthesis and development of Hsp90 inhibitor for the treatment of cancer. In this manuscript, the authors have compiled the Hsp90 inhibitors containing various heterocyclic rings such as Benzothiazone, Benzamide, Isoxazole, Pyrimidinone, Imidazole, Pyrimidine, Isatin, Quinazoline and Pyrazole etc. In this review, the authors incorporate the anticancer activity, Structure-Activity Relationship (SAR), Molecular Docking studies and Biological Evaluation of recently synthesized Hsp90 inhibitors. The authors have also added the clinical trial status of Hsp90 inhibitors and their potential use in cancer therapy. Overall, this review highlights the recent advancements in the field of Hsp90 inhibitor from 2020 to present. This review provides medicinal chemists with comprehensive insights into the development of Hsp90 inhibitors by guiding future research and drug discovery efforts in cancer.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144787961","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

Discovery of N-[2-(4-methylquinolin-2-yl)phenyl]acetamidine as a new potent nitric oxide synthase inhibitor against glioma progression. N-[2-（4-甲基喹啉-2-基）苯基]乙酰脒作为一种新的有效的抗胶质瘤进展的一氧化氮合酶抑制剂的发现。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-08-05 DOI: 10.1007/s11030-025-11309-0

Marialucia Gallorini, Rosa Amoroso, Amelia Cataldi, Cristina Maccallini

引用次数: 0

Construction of multifunctionalized (oxo)indoles via O-Selective interception of the zwitterionic intermediate with N=O. 用N=O选择性截取两性离子中间体构建多功能化（氧）吲哚。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-08-05 DOI: 10.1007/s11030-025-11279-3

Bao-Fan Wang, Ziming Qiu, Fei Lian, Mengke Zhang, Tai-Chen Liu, Zheng-Hui Kang, Jiu-Ling Li

引用次数: 0

Green chemistry for prostate health: exploring nature's toolbox against cancer, inflammation, and hyperplasia. 前列腺健康的绿色化学：探索抗癌、炎症和增生的自然工具箱。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-08-05 DOI: 10.1007/s11030-025-11305-4

Muavia Sarwar, Hafiz Muhammad Irfan, Alamgeer, Zeeshan Jabbar, Mulazim Hussain Asim, Muhammad Sajjad Hassan, Shoaib Nawaz

{"title":"Green chemistry for prostate health: exploring nature's toolbox against cancer, inflammation, and hyperplasia.","authors":"Muavia Sarwar, Hafiz Muhammad Irfan, Alamgeer, Zeeshan Jabbar, Mulazim Hussain Asim, Muhammad Sajjad Hassan, Shoaib Nawaz","doi":"10.1007/s11030-025-11305-4","DOIUrl":"https://doi.org/10.1007/s11030-025-11305-4","url":null,"abstract":"This study explores the potential of phytochemicals as an innovative and successful strategy for mitigating symptoms of benign prostatic hyperplasia and prostate cancer. Prostate cancer is the most prevalent and second highest cause of mortality for men, with over 121 million cases worldwide. Finding no comprehensive study on the impact of compounds from all phytochemical classes in the perspectives of prostate disorders, this study analyzed data from a variety of investigations, including preclinical studies, in vitro and in vivo experiments, clinical trials, and cell line investigations, as part of a review of the literature. With a focus on mechanisms such as aging and hormonal factor modulation, apoptosis induction, overcoming inflammation, metabolic crisis, and medication resistance while minimizing side effects, the search concentrated on the effects of natural substances on prostate hyperplastic and malignant cells. The sequel of the study is suggestive of strong anti-hyperplastic and anti-cancerous potentials of phytochemicals in prostate cells. These phytochemicals have the capacity to target several pathways, providing a multifaceted approach to the therapy of BPH and prostate cancer. Interestingly, a large number of these substances exhibit negligible side effects, suggesting their possible use in clinical settings. The study unveils the mechanistic role of compounds belonging to various phytochemical classes in improving life quality and longevity in men with BPH and prostate cancer. Besides associating the existing literature, outcomes of the study ignite encouraging prospects towards further advancements in drug discovery and anticipation for prompt therapies.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144783177","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

Recent advances in piperazine derivatives as antibacterial agents: a comprehensive review (2020-2024). 哌嗪衍生物抗菌药物研究进展综述（2020-2024）

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-08-04 DOI: 10.1007/s11030-025-11311-6

Kripa Patel, Moksh Shah, Khyati Patel, Afzal Nagani

引用次数: 0

Advanced drug-target interaction prediction using convolutional graph attention networks in expert systems. 专家系统中基于卷积图注意网络的药物-靶标相互作用预测。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-08-02 DOI: 10.1007/s11030-025-11290-8

R Mythili, N Parthiban

{"title":"Advanced drug-target interaction prediction using convolutional graph attention networks in expert systems.","authors":"R Mythili, N Parthiban","doi":"10.1007/s11030-025-11290-8","DOIUrl":"https://doi.org/10.1007/s11030-025-11290-8","url":null,"abstract":"Predicting drug-target interaction (DTI) is crucial in drug discovery and repurposing, as it significantly cuts the time and costs associated with traditional experimental methods. To address these challenges, this study introduces an advanced deep learning framework that integrates graph-based neural networks with novel feature selection mechanisms to improve DTI prediction accuracy. A Convolutional Multilayer Extreme Adversarial Graph Attention-based Neural Network (CMEAG-ANN), combined with a Fast Correlation-Based Gradient Naïve Bayes and Binary Pattern Selection (FC-GNBBPS) algorithm, is proposed for the robust and biologically meaningful feature extraction from DNA molecule-derived data. Using graph attention algorithms that capture complex relationships within molecular graphs, CMEAG-ANN effectively integrates structural and evolutionary aspects of drugs and target proteins. It uses both molecular fingerprints and PSSM-based annotations, ensuring a rich representation of chemical and biological information. Experimental evaluations on benchmark datasets, including approved_drug_target dataset, ImDrug dataset, DrugProt dataset, and Drug Combination Extraction Dataset, are compared with the CMEAG-ANN and the baseline models. The CMEAG-ANN model achieves an accuracy of 99.17%, precision of 99.11%, recall of 98.83%, F1-score of 98.96%, and specificity of 98.74%. This study highlights the model's effectiveness in improving the reliability and efficiency of DTI systems through biologically grounded feature selection.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768271","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

Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review. 预测小分子- mirna关联的机器学习方法：综合综述。

IF 3.9 2区化学

Molecular Diversity Pub Date : 2025-08-01 Epub Date: 2025-05-20 DOI: 10.1007/s11030-025-11211-9

Ashish Panghalia, Vikram Singh

{"title":"Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.","authors":"Ashish Panghalia, Vikram Singh","doi":"10.1007/s11030-025-11211-9","DOIUrl":"10.1007/s11030-025-11211-9","url":null,"abstract":"MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potential diagnostic and therapeutic targets that could be inhibited by small molecules (SMs). The knowledge of SM-miRNA associations (SMAs) is sparse, mainly because of the dynamic and less predictable 3D structures of miRNAs that restrict the high-throughput screening of SMs. Toward augmenting the costly and laborious experiments determining the SM-miRNA interactions, machine learning (ML) has emerged as a cost-effective and efficient platform. In this article, various aspects associated with the ML-guided predictions of SMAs are thoroughly reviewed. Firstly, a detailed account of the SMA data resources useful for algorithms training is provided, followed by an elaboration of various feature extraction methods and similarity measures utilized on SMs and miRNAs. Subsequent to a summary of the ML algorithms basics and a brief description of the performance measures, an exhaustive census of all the 32 ML-based SMA prediction methods developed so far is outlined. Distinctive features of these methods have been described by classifying them into six broad categories, namely, classical ML, deep learning, matrix factorization, network propagation, graph learning, and ensemble learning methods. Trend analyses are performed to investigate the patterns in ML algorithms usage and performance achievement in SMA prediction. Outlining key principles behind the up-to-date methodologies and comparing their accomplishments, this review offers valuable insights into critical areas for future research in ML-based SMA prediction.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3825-3856"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109420","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

Machine learning-based design, screening, and activity validation of topoisomerase I inhibitors. 拓扑异构酶I抑制剂基于机器学习的设计、筛选和活性验证。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-07-31 DOI: 10.1007/s11030-025-11295-3

Ya-Kun Zhang, Jian-Bo Tong, Jia-Le Li, Rong Wang, Yan-Rong Zeng

{"title":"Machine learning-based design, screening, and activity validation of topoisomerase I inhibitors.","authors":"Ya-Kun Zhang, Jian-Bo Tong, Jia-Le Li, Rong Wang, Yan-Rong Zeng","doi":"10.1007/s11030-025-11295-3","DOIUrl":"https://doi.org/10.1007/s11030-025-11295-3","url":null,"abstract":"Topoisomerase I (TOP I) plays a vital role in maintaining genomic stability and regulating cellular proliferation. Its overexpression in aggressive cancers such as lung, pancreatic, and breast malignancies highlights its value as a therapeutic target. However, the current TOP I inhibitors face limitations including poor hydrolytic stability, significant toxicity, and the emergence of drug resistance. To address these issues, this study developed a comprehensive QSAR framework that goes beyond traditional methods restricted by limited descriptors or single algorithms. A dataset of 550 high-activity compounds from ChEMBL, BindingDB, and Topscience was systematically screened to build thirty QSAR models combining five molecular fingerprint types with six advanced machine learning algorithms. An optimized artificial neural network model was then employed to rationally design 5938 candidate inhibitors using the sequential attachment-based fragment embedding (SAFE) methodology. These candidates underwent rigorous evaluation through activity prediction, drug-likeness assessment, and ADMET profiling, resulting in seven promising compounds. Among them, three were experimentally validated by MTT cytotoxicity assays, while four novel compounds were further characterized by molecular docking and molecular dynamics simulations. This integrative approach provides a robust theoretical foundation for the rational design and optimization of TOP I inhibitors, facilitating the development of targeted therapies against TOP I-associated cancers.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758893","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

Identifying dormancy-associated enzymes in Mycobacterium tuberculosis through a computational pipeline integrating flux balance analysis and metabolic modeling. 通过整合通量平衡分析和代谢模型的计算管道识别结核分枝杆菌中的休眠相关酶。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-07-29 DOI: 10.1007/s11030-025-11300-9

Mohd Imran, Ahmed S Alshrari, Abida Khan

{"title":"Identifying dormancy-associated enzymes in Mycobacterium tuberculosis through a computational pipeline integrating flux balance analysis and metabolic modeling.","authors":"Mohd Imran, Ahmed S Alshrari, Abida Khan","doi":"10.1007/s11030-025-11300-9","DOIUrl":"https://doi.org/10.1007/s11030-025-11300-9","url":null,"abstract":"Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains a critical global health challenge due to rising drug resistance and the pathogen's ability to persist in hostile host environments. Identifying novel molecular targets that underlie Mtb's unique survival mechanisms is essential for developing more effective therapies. In this study, we developed an integrative computational pipeline combining genome-scale metabolic modeling, flux balance analysis (FBA), comparative genomics, and network-based prioritization to uncover metabolic vulnerabilities specific to Mtb. Comparative analysis with the reductively evolved Mycobacterium leprae revealed significant differences in pathways involved in pantothenate biosynthesis (PanB), peptidoglycan synthesis (GlmU), and branched-chain amino acid metabolism (IlvN). These targets were prioritized based on gene essentiality, dormancy-associated expression, druggability, and absence of human homologs to maximize therapeutic selectivity. Molecular docking, followed by MM-GBSA binding free energy calculations, identified high-affinity ligands from LifeChemicals and ChEMBL libraries interacting strongly with active-site residues. Molecular dynamics simulations were performed to further validate target engagement and ligand retention, revealing stable conformational behavior and persistent protein-ligand interactions across 300 ns. Similarly, metabolite flux analysis and pathway enrichment highlighted adaptive rewiring in glycine, serine, pyruvate, and nitrogen metabolism, reflecting Mtb's persistence strategies under host-imposed stress. This study provides a robust, generalizable pipeline for pathogen-specific drug target and ligand discovery and supports the rational development of new therapies against drug-resistant tuberculosis.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740802","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

In silico identification of sclerostin inhibitors. 硬化蛋白抑制剂的计算机鉴定。

IF 3.8 2区化学

Molecular Diversity Pub Date : 2025-07-29 DOI: 10.1007/s11030-025-11298-0

Yusuf Şimşek, Sahra Setenay Baran, Erdal Ergünol, Altay Uludamar, Aylin Sepici Dinçel, Şakir Erkoç

{"title":"In silico identification of sclerostin inhibitors.","authors":"Yusuf Şimşek, Sahra Setenay Baran, Erdal Ergünol, Altay Uludamar, Aylin Sepici Dinçel, Şakir Erkoç","doi":"10.1007/s11030-025-11298-0","DOIUrl":"https://doi.org/10.1007/s11030-025-11298-0","url":null,"abstract":"Wnt/β-catenin signaling pathway plays a major role in the regulation of bone homeostasis. Sclerostin exhibits a high-affinity binding to the Wnt co-receptors LRP5/6 and therefore acts as an extracellular inhibitor of canonical Wnt signaling. Disruption of the interaction between LRP5/6 and sclerostin is essential for Wnt-related metabolic processes that can affect bone health. Consequently, we targeted the loop 2 region of sclerostin, which binds stably to LRP5/6, and employed a series of in silico approaches, including molecular docking and molecular dynamics simulations, to screen drug-like compounds from the DrugBank database. The loop 2 region of sclerostin is relatively flexible and mobile in solution. To enhance the accuracy of screening, we generated eight distinct conformers of sclerostin following initial molecular dynamics simulations. Subsequently, we applied virtual screening methods, including high-throughput virtual screening, standard precision, extra precision, and molecular mechanics generalized Born surface area calculations for each conformer. After merging hits, 50 compounds were further studied with molecular dynamics simulations and binding energy computations over the trajectories. Our results revealed that the compounds DB02675, DB15238, DB04226, DB03325, and DB05644 exhibit inhibitory activity on the loop 2 region of sclerostin.","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144726405","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