Molecular Diversity最新文献

{"title":"Unleashing the potential of traditional Chinese medicine: a computational approach to discovering drug targets utilizing the CSLN and molecular dynamics.","authors":"Qi Geng, Pengcheng Zhao, Zhiwen Cao, Zhenyi Wu, Changqi Shi, Lulu Zhang, Lan Yan, Xiaomeng Zhang, Peipei Lu, Jianyu Shi, Cheng Lu","doi":"10.1007/s11030-025-11177-8","DOIUrl":"https://doi.org/10.1007/s11030-025-11177-8","url":null,"abstract":"<p><p>The diverse chemical components of traditional Chinese medicine (TCM) exhibit significant therapeutic potential; however, the action mechanisms of these compounds often remain unclear. The use of drug-target prediction can aid in identifying the specific targets of TCM, thereby revealing their bioactivity and mechanisms. The efficiency, cost-effectiveness, and powerful predictive capabilities of artificial intelligence algorithms have led to their emergence as effective tools for accelerating drug-target interaction analysis. To systematically investigate TCM interaction mechanisms, we integrated cosine‑correlation and similarity‑comparison of local network (CSLN) and molecular dynamics (MD) simulations. The CSLN algorithm predicts that 11-beta-hydroxysteroid dehydrogenase-1 (HSD11B1) serves as a common target for the synergistic effects of triptolide (TP) and glycyrrhizic acid (GA). MD simulations indicate that both TP and GA can maintain stable interactions with HSD11B1 and form a common binding hot region. Surface plasmon resonance (SPR) experiments reveal that both TP and GA can effectively bind to HSD11B1, with binding constants of 29.21 μM and 31.75 μM, respectively. When used in combination, the binding constant is 5.74 μM. The combination of CSLN and MD simulations represents an effective tool for the initial analysis and simulation of interaction patterns between TCM and their targets at the computational level. These findings enhance our understanding of the interaction mechanisms between drugs.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957628","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":"https://doi.org/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":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-02","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":"Correction: Discovery of novel 4-trifluoromethyl-2-anilinoquinoline derivatives as potential anti-cancer agents targeting SGK1","authors":"Guangcan Xu,&nbsp;Lanlan Li,&nbsp;Mengfan Lv,&nbsp;Cheng Li,&nbsp;Jia Yu,&nbsp;Xiaoping Zeng,&nbsp;Xueling Meng,&nbsp;Gang Yu,&nbsp;Kun Liu,&nbsp;Sha Cheng,&nbsp;Heng Luo,&nbsp;Bixue Xu","doi":"10.1007/s11030-025-11181-y","DOIUrl":"10.1007/s11030-025-11181-y","url":null,"abstract":"","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":"29 3","pages":"1967 - 1968"},"PeriodicalIF":3.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143955123","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: Python tools for studying biomolecules and drug design.","authors":"Fedor V Ryzhkov, Yuliya E Ryzhkova, Michail N Elinson","doi":"10.1007/s11030-025-11199-2","DOIUrl":"https://doi.org/10.1007/s11030-025-11199-2","url":null,"abstract":"<p><p>The increasing adoption of computational methods and artificial intelligence in scientific research has led to a growing interest in versatile tools like Python. In the fields of medical chemistry, biochemistry, and bioinformatics, Python has emerged as a key language for tackling complex challenges. It is used to solve various tasks, such as drug discovery, high-throughput and virtual screening, protein and genome analysis, and predicting drug efficacy. This review presents a list of tools for these tasks, including scripts, libraries, and ready-made programs, and serves as a starting point for scientists wishing to apply automation or optimization to routine tasks in medical chemistry and bioinformatics.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143956964","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":"GLP-1R agonists: recent advances, current gaps, and future challenges.","authors":"Anukriti Saran, Riya Raisinghani, Sarvesh Paliwal, Swapnil Sharma","doi":"10.1007/s11030-025-11195-6","DOIUrl":"https://doi.org/10.1007/s11030-025-11195-6","url":null,"abstract":"<p><p>Glucagon-like peptide-1 receptor agonists (GLP-1RAs) emerged as a promising class of drugs and have been shown to be effective as a key regulator in managing glucose metabolism-associated diseases such as type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), cardiovascular effects, nephrological complications, diabetes, non-alcoholic fatty liver (NAFLD), as well as to control obesity. A few drugs included in GLP-1RA class are liraglutide, exenatide, and semaglutide. Most recent drug that is available in both oral and subcutaneous forms is semaglutide. Available, withdrawn, and investigational GLP-RAs are listed in this paper. This review article will also explore common side effects and safety profiles of both long-acting and short-acting GLP-1 RAs. Additionally, it will highlight the recent advances and ongoing challenges in the field of drug discovery related to GLP-1 receptor agonists.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961708","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":"Computational approach for the evaluation of sesquiterpene lactone as a modulator of cannabinoid receptor type 2 for neurodegenerative disease prophylactics.","authors":"Ram Lal Swagat Shrestha, Ashika Tamang, Sujan Dhital, Nirmal Parajuli, Manila Poudel, Safal Adhikari, Shiva M C, Aakar Shrestha, Timila Shrestha, Samjhana Bharati, Binita Maharjan, Bishnu P Marasini, Jhashanath Adhikari Subin","doi":"10.1007/s11030-025-11191-w","DOIUrl":"10.1007/s11030-025-11191-w","url":null,"abstract":"<p><p>Neurodegenerative diseases represent a major global health challenge, with cannabinoid receptor type 2 (CB2) emerging as a promising therapeutic target for its role in inflammation modulation and neuroprotection. Sesquiterpene lactone is a class of natural compounds with diverse molecular structures and known biological activities. This study aimed to explore sesquiterpene lactones for their potential as CB2 modulators using computational approaches such as molecular docking, molecular dynamics simulations (MDS), and ADMET predictions, to identify the promising candidates for neurodegenerative disease prophylactics. Out of 85 sesquiterpene lactones evaluated, podachaenin (PubChem CID: 15,828,229) exhibited the highest binding affinity to CB2 (- 12.242 kcal/mol), outperforming that of the native ligand (- 12.168 kcal/mol) and reference drugs apomorphine (- 9.482 kcal/mol), dantrolene (- 8.861 kcal/mol), and galantamine (- 9.689 kcal/mol). Hydrogen bonds as well as alkyl, Pi-alkyl, and van der Waal's interactions were present in the CB2-podachaenin complex providing structural intactness. MDS of 500 ns evaluated the stability of the protein-ligand complex and receptor structure in apo form through geometrical parameters: root mean square deviation, root mean square fluctuation, radius of gyration, solvent accessible surface area, and hydrogen bond length. Additionally, the binding free energy change calculation supplemented the initial inferences in terms of thermodynamic stability with a value of - 40.92 ± 4.56 kcal/mol. ADMET profiling also indicated favorable pharmacokinetic and pharmacodynamic properties, similar to that of the reference drugs. The preliminary results identified podachaenin as a possible CB2 modulator for treating neurodegenerative diseases and could be a hit compound in neuro-drug design. Further in vivo and in vitro studies are suggested to validate it as a hit candidate.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143957280","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":"In silico and in vitro analysis of diethyl phthalate as a quorum sensing inhibitor and its antitumor evaluation against MDA-MB-231 cell lines.","authors":"Nagasundaram Rashiya, Jeyachandran Sangavi, Nagarajan Padmini, Kulanthaivel Langeswaran, Arun Alagarsamy, Gopal Selvakumar, Muthupandian Saravanan","doi":"10.1007/s11030-025-11202-w","DOIUrl":"https://doi.org/10.1007/s11030-025-11202-w","url":null,"abstract":"<p><p>Diethyl phthalate (DEP), a phthalate acid ester present in naturally occurring substances of living forms including flora, fauna, and microbes, particularly those of the Streptomyces genus, functions as an allelochemical, antibiotic, or pesticide to aid donor species in their adaption. In this in silico experiment, DEP was utilized as a quorum sensing inhibitor (QSI) against the quorum sensing (QS) protein of Chromobacterium violaceum and Pseudomonas aeruginosa such as CviR and LasR. We identified that quorum sensing system of both the organisms tested may be blocked due to the utilization of DEP, which contributes to our knowledge of the molecular process underlying QS-regulated behaviors. In vitro testing of the DEP anticancer efficacy over MDA-MB-231 cells, which revealed considerable cytotoxicity with an IC₅₀ value found at 65 µg/mL. DEP reduced the development of MDA-MB-231 cells and caused cell death in a based on concentration. As a result, DEP could be a potential therapeutic alternative for microbial pathogens that create biofilms.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143951706","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":"Co-development of efflux pump inhibitors with antibiotics on targeting structural and mutational aspects of AcrB subunit.","authors":"Shweta Singh Chauhan, Tanya Jamal, Anshika Gupta, Ramakrishnan Parthasarathi","doi":"10.1007/s11030-025-11204-8","DOIUrl":"https://doi.org/10.1007/s11030-025-11204-8","url":null,"abstract":"<p><p>The rise of antimicrobial resistance (AMR) of the routinely used antibiotics is ineffective against drug-resistant pathogenic strains of Escherichia coli, set off with limited treatment choices, costs, and increasing mortality rates. Multidrug efflux pumps have been identified as crucial determinants of AMR, flushing numerous antibiotics from cells in a non-specific way, and have emerged as promising drug targets to overcome AMR. Herein, the work focuses on determining structural and mutational insights of tripartite efflux pump subunit AcrB by executing multiple sequence alignment (MSA); the residues 615 and 617 at the substrate-binding site were identified mutated from an aromatic amino acid, phenylalanine, to an aliphatic amino acid, alanine. The study proceeded with the co-development of AcrB antagonist's by applying pharmacokinetic parameters filters to 40,613 natural compounds and molecular docking of single compounds, multiple ligand simultaneous docking (MLSD), molecular dynamics (MD) simulations, principal component analysis (PCA), and free energy landscape (FEL) analysis by considering resistant antibiotics. The identified mutations in the AcrB subunit are responsible for upregulating the activity of the AcrAB-TolC efflux pump and leading to a reduced concentration of antibiotics in the bacterial cytoplasm, ultimately increasing antibiotic resistance. Furthermore, based upon compound screening against target AcrB, 3-Hydroxyfumiquinazoline A shows competitive interaction with the antibiotic Erythromycin. A similar interaction pattern was observed between Sungucine and Cheatoglobosin D with Novobiocin while Procheatoglobosin I and Chaetoglobosin Q with Fusidic acid. Our findings highlight a novel class of efflux pump inhibitors (EPIs) that effectively antagonize the AcrB subunit and could serve as novel adjuvant alternatives for reviving antibiotic activity in resistant bacteria.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959283","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":"Exploring Lamiaceae diterpenoids as potential multitarget therapeutics for leishmaniasis and chagas disease.","authors":"Renata Priscila Barros de Menezes, Edileuza Bezerra de Assis, Natália Ferreira de Sousa, Jullyanne Maria Saraiva de Souza, Klinger Antônio da França Rodrigues, Luciana Scotti, Josean Fechine Tavares, Marcelo Sobral da Silva, Marcus Tullius Scotti","doi":"10.1007/s11030-025-11200-y","DOIUrl":"https://doi.org/10.1007/s11030-025-11200-y","url":null,"abstract":"<p><p>Neglected tropical diseases such as Leishmaniasis and Chagas disease remain critical public health challenges. This study applied ligand-based virtual screening to a dataset of 4,150 secondary metabolites from the Lamiaceae family, aiming to identify multitarget molecules against four Leishmania species (L. infantum, L. donovani, L. amazonensis, and L. braziliensis) and Trypanosoma cruzi forms. Random forest models exhibited high accuracy (over 72%), leading to the identification of 82 molecules with potential multitarget activity across five of six predictive models. Nineteen prioritized molecules were subjected to molecular docking simulations targeting key enzymes-including sterol 14-alpha demethylase, glucose-6-phosphate dehydrogenase, dihydroorotate dehydrogenase, nucleoside diphosphate kinase, tryparedoxin peroxidase, and cruzain-with compounds 12, 18, and 19 exhibiting a high binding affinity across multiple targets. In vitro assays confirmed the predicted activity of selected molecules (3, 4, and 5) against Leishmania and T. cruzi. Importantly, these molecules represent novel findings, with antileishmanial or antitrypanosomal activities that have not been previously reported. The results highlight their potential as multitarget therapeutic candidates for neglected tropical diseases, paving the way for further biological evaluation and development.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960940","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":"Graph-Aware AURALSTM: An Attentive Unified Representation Architecture with BiLSTM for Enhanced Molecular Property Prediction.","authors":"Muhammed Ali Pala","doi":"10.1007/s11030-025-11197-4","DOIUrl":"https://doi.org/10.1007/s11030-025-11197-4","url":null,"abstract":"<p><p>Predicting molecular properties with high accuracy is essential across scientific fields, from drug discovery and biotechnology to materials science and environmental research. In biomedical sciences, accurate molecular property prediction is crucial for elucidating disease mechanisms, identifying potential drug candidates, and optimising various processes. However, existing approaches, often based on low-dimensional representations, fail to capture the intricate spatial and structural complexities of molecular data. This study introduces a novel hybrid deep learning model, the Graph-Aware AURA-LSTM (Attentive Unified Representation Architecture-Long Short-Term Memory), designed to determine molecular properties with unprecedented accuracy using advanced graphical representations. AURA-LSTM combines multiple Graph Neural Network (GNN) architectures, specifically Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Isomorphism Networks (GINs), in a parallel structure to comprehensively capture the multidimensional structural features of molecules. Within this architecture, GCNs incorporate local structural relationships, GATs apply attention mechanisms to highlight critical structural elements, and GINs capture intricate molecular details through isomorphic distinction, resulting in a richly detailed feature matrix. The feature layer then processes this BiLSTM matrix, which evaluates temporal relationships to enhance molecular feature classification. Evaluated on eight benchmark datasets, AURA-LSTM demonstrated superior performance, consistently achieving over 90% accuracy and outperforming state-of-the-art methods. These results position AURA-LSTM as a robust tool for molecular feature classification, uniquely capable of integrating temporally aware insights from distinct GNN architectures.</p>","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":"143955357","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}