Molecular Diversity最新文献

{"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":"10.1007/s11030-025-11194-7","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3537-3554"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","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}

{"title":"Identification of STAT3 phosphorylation inhibitors using generative deep learning, virtual screening, molecular dynamics simulations, and biological evaluation for non-small cell lung cancer therapy.","authors":"Weiji Cai, Beier Jiang, Yichen Yin, Lei Ma, Tao Li, Jing Chen","doi":"10.1007/s11030-024-11067-5","DOIUrl":"10.1007/s11030-024-11067-5","url":null,"abstract":"<p><p>The development of phosphorylation-suppressing inhibitors targeting Signal Transducer and Activator of Transcription 3 (STAT3) represents a promising therapeutic strategy for non-small cell lung cancer (NSCLC). In this study, a generative model was developed using transfer learning and virtual screening, leveraging a comprehensive dataset of STAT3 inhibitors to explore the chemical space for novel candidates. This approach yielded a chemically diverse library of compounds, which were prioritized through molecular docking and molecular dynamics (MD) simulations. Among the identified candidates, the HG110 molecule demonstrated potent suppression of STAT3 phosphorylation at Tyr705 and inhibited its nuclear translocation in IL6-stimulated H441 cells. Rigorous MD simulations further confirmed the stability and interaction profiles of top candidates within the STAT3 binding site. Notably, HG106 and HG110 exhibited superior binding affinities and stable conformations, with favorable interactions involving key residues in the STAT3 binding pocket, outperforming known inhibitors. These findings underscore the potential of generative deep learning to expedite the discovery of selective STAT3 inhibitors, providing a compelling pathway for advancing NSCLC therapies.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3189-3205"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multiscale molecular structural neural network for molecular property prediction.","authors":"Zhiwei Shi, Miao Ma, Hanyang Ning, Bo Yang, Jingshuang Dang","doi":"10.1007/s11030-024-11100-7","DOIUrl":"10.1007/s11030-024-11100-7","url":null,"abstract":"<p><p>Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded \"message passing\" structures at the atomic scale and spatial feature information \"encoder-decoder\" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values. Experimental results in the authoritative small molecule dataset QM9 and the macromolecular protein database PDBbind demonstrate that MMSNet has optimal prediction accuracy, model complexity, and generalizability compared with more than ten existing state-of-the-art (SOTA) models in a variety of different types of prediction tasks; it has a great potential for downstream tasks such as chemical research, drug discovery, and material design.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3273-3292"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035840","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}

{"title":"Cangrelor and AVN-944 as repurposable candidate drugs for hMPV: analysis entailed by AI-driven in silico approach.","authors":"Amritha Thaikkad, Fathimath Henna, Sonet Daniel Thomas, Levin John, Rajesh Raju, Abhithaj Jayanandan","doi":"10.1007/s11030-025-11206-6","DOIUrl":"10.1007/s11030-025-11206-6","url":null,"abstract":"<p><p>Human metapneumovirus (hMPV) primarily causes respiratory tract infections in young children and older adults. According to the 2024 Human Pneumonia Etiology Research for Child Health (PERCH) study, hMPV is the second leading common cause of pneumonia in children under five in Asia and Africa. The virus encodes nine proteins, including the essential Fusion (F) and G glycoproteins, which facilitate entry to the host cells. Currently, there are no approved vaccines or antiviral treatments for hMPV; supportive care is the primary way it is managed. Hence, this study focuses on the F protein as a therapeutic target to find a repurposable drug to fight hMPV. Refolding of the F protein and its binding to heparan sulfate enable hMPV infection. Heparin sulfate is important for hMPV binding, and we have found that cangrelor and AVN 944 can prevent the fusion of membranes. We developed a deep learning-based pharmacophore to identify potential drugs targeting hMPV, from which we could narrowed a list of 2400 FDA-approved drugs and 255 antiviral drugs to 792 and 72 drugs, respectively. We then conducted quantitative validation using the ROC curve. Further virtual screening of the drugs was performed, leading us to select the one with the highest docking score. The validation of the deep learning prediction in virtual screening Pearson correlation was done. Further, the MD simulation of these drugs confirmed that the protein-drug complex stability remained in dynamic condition. Further, the stability of protein-drug complexes than unbound protein was confirmed by Free Energy Landscape and Dynamic Cross Correlation Matrices. Further in vitro and in vivo experiments need to determine the efficacy of the identified candidates.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3587-3605"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143956901","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}

{"title":"Identify critical genes of breast cancer and corresponding leading natural product compounds of potential therapeutic targets.","authors":"Xiaokai Fan, Le Xin, Xuan Yu, Maoxuan Liu, Joong Sup Shim, Gui Yang, Liang Chen","doi":"10.1007/s11030-024-11035-z","DOIUrl":"10.1007/s11030-024-11035-z","url":null,"abstract":"<p><p>Breast cancer is a leading cause of cancer mortality among women globally, with over 2.26 million new cases annually, according to GLOBOCAN 2020. This accounts for approximately 25% of all new female cancers and 15.5% of female cancer deaths. To address this critical public health challenge, we conducted a multi-omics study aimed at identifying hub genes, therapeutic targets, and potential natural product-based therapies. We employed weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis to pinpoint hub genes in breast cancer. Regulatory networks for these genes were constructed by re-analyzing chromatin immunoprecipitation sequencing (ChIP-seq) data from breast cancer cell lines. Additionally, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) were utilized to characterize hub gene expression profiles and their relationships with immune cell clusters and tumor microenvironments. Survival analysis based on mRNA and protein expression levels identified prognostic factors and potential therapeutic targets. Lastly, large-scale virtual screening of natural product compounds revealed leading compounds that target squalene epoxidase (SQLE). Our multi-omics analysis paves the way for more effective clinical treatments for breast cancer.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3009-3022"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799049","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}

{"title":"Machine learning-based screening and molecular simulations for discovering novel PARP-1 inhibitors targeting DNA repair mechanisms for breast cancer therapy.","authors":"Muhammad Shahab, Muhammad Waqas, Aamir Fahira, Bharat Prasad Sharma, Haoke Zhang, Guojun Zheng, Zunnan Huang","doi":"10.1007/s11030-025-11119-4","DOIUrl":"10.1007/s11030-025-11119-4","url":null,"abstract":"<p><p>Cancer remains one of the leading causes of death worldwide, with the rising incidence of breast cancer being a significant public health concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as a promising therapeutic target for breast cancer treatment due to its crucial role in DNA repair. This study aimed to discover novel, targeted, and non-toxic PARP-1 inhibitors using an integrated approach that combines machine learning-based screening, molecular docking simulations, and quantum mechanical calculations. We trained a widely used machine learning models, Random Forest, using bioactivity data from known PARP-1 inhibitors. After evaluating the performance, it was used to screen an FDA-approved drug library, successfully identifying Atazanavir, Brexpiprazole, Raltegravir, and Nisoldipine as potential PARP-1 inhibitors. These compounds were further validated through molecular docking and all-atom molecular dynamics simulations, highlighting their potential for breast cancer therapy. The binding free energies indicated that Atazanavir at - 41.86 kJ/mol and Brexpiprazole at - 45.44 kJ/mol exhibited superior binding affinity compared to the control drug at - 30.42 kJ/mol, highlighting their promise as candidates for breast cancer therapy. Subsequent optimized geometries and electron density mappings of the two molecular structures revealed a Gibbs free energy of - 2334.610 Ha for the first molecule and - 1682.278316 Ha for the second, confirming enhanced stability compared to the standard drug. This study not only highlights the efficacy of machine learning in drug discovery but also underscores the importance of quantum mechanics in validating molecular stability, setting a robust foundation for future pharmacological explorations. Additionally, this approach could revolutionize the drug repurposing process by significantly reducing the time and cost associated with traditional drug development methods. Our results establish a promising basis for subsequent research aimed at optimizing these PARP-1 inhibitors for clinical use, potentially offering more effective treatment options for breast cancer patients.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3323-3343"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143078359","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}

{"title":"Enhancing PI3Kγ inhibitor discovery: a machine learning-based virtual screening approach integrating pharmacophores, docking, and molecular descriptors.","authors":"Lei Jia, Lei Xu, Yanfei Cai, Yun Chen, Jian Jin, Li Yu, Jingyu Zhu","doi":"10.1007/s11030-025-11216-4","DOIUrl":"10.1007/s11030-025-11216-4","url":null,"abstract":"<p><p>PI3Kγ is a lipid kinase that is expressed primarily in leukocytes and plays a significant role in tumors, inflammation, and autoimmune diseases. Consequently, considerable attention has been given to the development of pharmacological inhibitors of PI3Kγ. Recently, machine learning-based virtual screening approaches have been increasingly applied in new drug discovery research, potentially providing innovative strategies for the development of PI3Kγ inhibitors. Thus, in this study, we developed a naïve Bayesian classification (NBC) model that integrates molecular descriptors, molecular fingerprints, molecular docking, and pharmacophore models for virtual screening of the PI3Kγ protein. The validation results indicated that the optimal model demonstrated significant potential for differentiating between active and inactive compounds, as well as a reliable ability to identify true PI3Kγ inhibitors with defined biological activity. Additionally, the optimal NBC model provided favorable and unfavorable fragments for PI3Kγ inhibitors, which will help guide the design and discovery of novel PI3Kγ inhibitors. Finally, the optimal NBC model was employed to perform virtual screening on the ChEMBL database, resulting in the identification of several compounds with high potential as PI3Kγ inhibitors. We anticipate that the developed machine learning-based virtual screening approach will offer valuable insights and guidance for the development of novel PI3Kγ inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3661-3678"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958003","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}

{"title":"Unraveling potent Glycyrrhiza glabra flavonoids as AKT1 inhibitors using network pharmacology and machine learning-assisted QSAR.","authors":"Desu Gayathri Niharika, Punam Salaria, Amarendar Reddy M","doi":"10.1007/s11030-025-11210-w","DOIUrl":"10.1007/s11030-025-11210-w","url":null,"abstract":"<p><p>Glycyrrhiza glabra (G. glabra) phytocompounds have been reported to interact with neurological targets, including those implicated in epilepsy, and may modulate epilepsy-related targets. While substantial evidence supports their potential antiepileptic effects, the underlying molecular mechanisms remain unclear. This study aims to elucidate the molecular mechanism of G. glabra phytocompounds by integrating network pharmacology and machine learning (ML)-based quantitative structure-activity relationship (QSAR) techniques. Network pharmacology analysis identified AKT1 as a key epilepsy-related target, and four ML-based 2D-QSAR models were developed using AKT1 inhibitors. The developed models underwent comprehensive validation, including internal and external validation, Y-randomization, statistical analysis, and applicability domain assessment to ensure robustness and reliability. Among them, the Multilayer Perceptron (MLP) model excelled as the most robust and demonstrated superior predictive ability with a correlation coefficient r²_training = 0.95, r²_test = 0.84, and cross-validation coefficient q² = 0.72. The MLP model accurately predicted pIC₅₀ values of phytoflavonoids, leading to the identification of 19 active molecules through the activity atlas model. ADME and drug-likeliness screening narrowed the selection to eleven promising compounds for further docking analysis. Molecular docking highlighted glabranin and 3'-hydroxy-4'-O-methylglabridin as top lead compounds with a binding energy of - 5.75 and - 5.37 kcal/mol, respectively. Additionally, 400 ns molecular dynamics simulation confirmed the structural and conformational stability of the glabranin-AKT1 complex, further reinforced by Principal Component Analysis, free energy landscapes, and Molecular Mechanics Poisson-Boltzmann/Generalized Born Surface Area. Collectively, these findings underscore the potential of G. glabra phytocompounds as promising antiepileptic candidates, paving the way for future advancements in this field.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3607-3635"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958301","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}

{"title":"Structural bioinformatics insights into UDP-galactopyranose mutase (UGM) as a novel drug target for antifilarial therapy against human filarial parasite Brugia malayi.","authors":"Arasu Muneeshwari, Natarajan Sampath","doi":"10.1007/s11030-025-11304-5","DOIUrl":"https://doi.org/10.1007/s11030-025-11304-5","url":null,"abstract":"<p><p>A flavoenzyme, UDP-galactopyranose mutase (UGM), serves as a pivotal enzyme catalysing the conversion of UDP-galactopyranose (galP) into UDP-galactofuranose (galF), a metabolite exclusively present in pathogenic microorganisms, including filarial parasites. The galF plays a critical role in various pathogenic processes, like cell wall biosynthesis, virulence enhancement, and cuticle formation in filarial parasites. Notably, the absence of galF in humans renders, UGM an attractive and promising drug target for developing potent antifilarial therapeutics. In this study, we employed advanced bioinformatics approaches to identify effective antifilarial drug candidates. The UGM enzyme from Brugia malayi (BmUGM) was meticulously modelled and subsequently utilized for molecular docking studies against 20 triazolothiadiazine analogues using the AutoDock program. Among these, eight compounds exhibiting high binding affinities, ranging from - 8.7 to - 10.5 kcal/mol, were selected for further protein-ligand MD simulations. Post-simulation analyses, encompassing MM-PBSA and binding free energy decomposition, demonstrated that two triazolothiadiazine analogues, namely D4 and D8, exhibited exceptionally high binding free energies of - 29.76 kcal/mol and - 27.50 kcal/mol, respectively. These values exceeded the binding free energy of the natural substrate galP, which was calculated at - 20.01 kcal/mol. Furthermore, binding free energy decomposition analysis pinpointed critical binding site residues Tyr168, Trp184, Tyr326, Tyr335, Arg336, Tyr405, and Gln475 as essential mediators of the protein-ligand interactions. Additionally, ADMET and DFT quantum mechanical calculations confirmed that the triazolothiadiazine analogues exhibit low toxicity profiles and favourable chemical reactivity. Based on these findings, we propose that the identified ligand molecules hold potential as potent inhibitors of BmUGM, with broad-spectrum efficacy against all life stages of filarial parasites.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144697247","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}

{"title":"Deoxynojirimycin derivatives as potent α-glucosidase inhibitors: in silico ADMET evaluation, molecular dynamics and in vitro validation studies.","authors":"Fariya Khan, Suhail Ahmad, Khwaja Osama, Alvina Farooqui, Ajay Kumar, Salman Akhtar","doi":"10.1007/s11030-025-11307-2","DOIUrl":"https://doi.org/10.1007/s11030-025-11307-2","url":null,"abstract":"<p><p>α-Glucosidase plays a critical role in digesting carbohydrates, leading to an increase in postprandial glucose levels, which contributes to the development and progression of diabetes. By inhibiting this enzyme, it is possible to manage postprandial hyperglycemia, thereby reducing the risk of developing or exacerbating diabetes. The primary aim of our study was to identify and evaluate potential α-glucosidase inhibitors from a series of deoxynojirimycin derivatives, using a combination of binding affinity analysis, simulation studies, and in vitro experiments. 371 deoxynojirimycin analogs were screened based on their compliance with Lipinski's Rule of Five and favorable absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters. Among these, compound MG257 (C₁₀H₂₁NO₄) stood out due to its strong binding interactions with the active site residues of α-glucosidase, as demonstrated through virtual screening and docking studies. In our in vitro analysis, MG257 (C₁₀H₂₁NO₄) demonstrated a notably potent α-glucosidase inhibitory activity with an IC₅₀ value of 0.44 ± 0.18 µM, surpassing the standard inhibitor miglitol, which exhibited an IC₅₀ of 0.64 ± 0.26 µM. Furthermore, molecular dynamics simulations conducted over 100 ns revealed that MG257 maintained excellent stability, further supporting its potential as a reliable inhibitor. Enzyme kinetics studies also confirmed that MG257 inhibits α-glucosidase competitively, reinforcing the findings from the molecular docking and simulation data. These comprehensive results, combining in silico and in vitro approaches, underscore the drug-likeness of MG257 and its promising pharmacokinetic profile. In conclusion, our findings suggest that MG257 (C₁₀H₂₁NO₄) is a potent α-glucosidase inhibitor with significant potential as a novel therapeutic agent for the management of Type 2 diabetes, warranting further research and development.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706026","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}