Molecular Diversity最新文献

{"title":"MedKG: enabling drug discovery through a unified biomedical knowledge graph.","authors":"Madhavi Kumari, Rohit Chauhan, Prabha Garg","doi":"10.1007/s11030-025-11164-z","DOIUrl":"10.1007/s11030-025-11164-z","url":null,"abstract":"<p><p>Biomedical knowledge graphs have emerged as powerful tools for drug discovery, but existing platforms often suffer from outdated information, limited accessibility, and insufficient integration of complex data. This study presents MedKG, a comprehensive and continuously updated knowledge graph designed to address these challenges in precision medicine and drug discovery. MedKG integrates data from 35 authoritative sources, encompassing 34 node types and 79 relationships. A Continuous Integration/Continuous Update pipeline ensures MedKG remains current, addressing a critical limitation of static knowledge bases. The integration of molecular embeddings enhances semantic analysis capabilities, bridging the gap between chemical structures and biological entities. To demonstrate MedKG's utility, a novel hybrid Relational Graph Convolutional Network for disease-drug link prediction, MedLINK was developed and used in case studies on clinical trial data for disease drug link prediction. Furthermore, a web-based application with user-friendly APIs and visualization tools was built, making MedKG accessible to both technical and non-technical users, which is freely available at http://pitools.niper.ac.in/medkg/.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3465-3483"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630160","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":"Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.","authors":"Yueying Yang, Yuxin Huang, Hanxiao Shen, Ding Wang, Zhen Liu, Wei Zhu, Qing Liu","doi":"10.1007/s11030-025-11145-2","DOIUrl":"10.1007/s11030-025-11145-2","url":null,"abstract":"<p><p>Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certain cancers, while clinically approved FXR antagonists remain unavailable and underrepresented in current treatment strategies. To address this, we developed deep learning models for predicting FXR antagonistic activity (ANTCL) and toxicity (TOXCL). Screening 217,345 compounds from the HMDB database identified eleven human metabolite candidates with significant FXR binding potential. Molecular dynamics simulations and binding free energy calculations revealed five more stable complexes compared to the reference compound Gly-MCA, with HMDB0253354 (Fulvestrant) and HMDB0242367 (ZM 189154) standing out for their binding free energies. Hydrophobic interactions, particularly involving residues MET328, PHE329, and ALA291, contributed to their stability. These results demonstrate the effectiveness of deep learning in FXR antagonist discovery and highlight the potential of HMDB0253354 and HMDB0242367 as promising candidates for metabolic disease treatment.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3391-3409"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762606","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 screening of umami tastants using deep learning.","authors":"Prantar Dutta, Kishore Gajula, Nitu Verma, Deepak Jain, Rakesh Gupta, Beena Rai","doi":"10.1007/s11030-024-11006-4","DOIUrl":"10.1007/s11030-024-11006-4","url":null,"abstract":"<p><p>Umami, a fundamental human taste modality, refers to the savory flavors in meats and broths, often associated with monosodium glutamate and protein richness. With limited knowledge of umami molecules, the food industry seeks efficient approaches for identifying novel tastants. In this study, we have devised a virtual screening pipeline for identifying highly potent umami tastants from large molecular databases. We curated the most extensive classification dataset containing 439 umami and 428 non-umami molecules and trained a transformer-based architecture to differentiate between the two classes, achieving 93% accuracy. Additionally, we built a neural network model for predicting the potency of umami compounds, the first effort of its kind. The classification and potency prediction models were combined with similarity analysis and toxicity screening to build an end-to-end virtual framework for the rational discovery of novel tastants. We applied this framework to the FooDB database containing around 70,000 molecules as an illustrative use case for screening potent umami compounds. The screened molecules were validated using molecular docking with the umami taste receptor. This study demonstrates the potential of data-driven methods in discovering new tastants from structural and chemical features of molecules and proposes an efficient implementation for industrial applications.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"2979-2993"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455377","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 models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1.","authors":"Anantha Krishnan Dhanabalan, Velmurugan Devadasan, Jebiti Haribabu, Gunasekaran Krishnasamy","doi":"10.1007/s11030-024-10997-4","DOIUrl":"10.1007/s11030-024-10997-4","url":null,"abstract":"<p><p>Sphingosine kinases (SphKs) are a group of important enzymes that circulate at low micromolar concentrations in mammals and have received considerable attention due to the roles they play in a broad array of biological processes including apoptosis, mutagenesis, lymphocyte migration, radio- and chemo-sensitization, and angiogenesis. In the present study, we constructed three classification models by four machine learning (ML) algorithms including naive bayes (NB), support vector machine (SVM), logistic regression, and random forest from 395 compounds. The generated ML models were validated by fivefold cross validation. Five different scaffold hit fragments resulted from SVM model-based virtual screening and docking results indicate that all the five fragments exhibit common hydrogen bond interaction a catalytic residue of SphK1. Further, molecular dynamics (MD) simulations and binding free energy calculation had been carried out with the identified five fragment leads and three cocrystal inhibitors. The best 15 fragments were selected. Molecular dynamics (MD) simulations showed that among these compounds, 7 compounds have favorable binding energy compared with cocrystal inhibitors. Hence, the study showed that the present lead fragments could act as potential inhibitors against therapeutic target of cancers and neurodegenerative disorders.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"2945-2977"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455379","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":"Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.","authors":"Mohammadreza Torabi, Soroush Sardari, Alejandro Rodríguez-Martínez, Nooshin Arabi, Horacio Pérez-Sánchez, Fahimeh Ghasemi","doi":"10.1007/s11030-025-11214-6","DOIUrl":"10.1007/s11030-025-11214-6","url":null,"abstract":"<p><p>Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; hence, capillary sprouting occurs from parental vessels, a process known as angiogenesis. Although multiple cellular pathways have been identified, controlling them with one single biomolecule as a multi-target inhibitor could be an attractive strategy for reducing medication side effects. Three critical pathways in angiogenesis have been identified, which are activated by the vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), and epidermal growth factor receptor (EGFR). This study aimed to develop a methodology to discover multi-target inhibitors among over 2000 FDA-approved drugs. Hence, a novel ensemble approach was employed, comprising classification and regression models. First, three different deep autoencoder classifications were generated for each target individually. The top 100 trained models were selected for the high-throughput virtual screening step. After that, all identified molecules with a probability of more than 0.9 in more than 70% of the models were removed to ensure accurate consideration in the regression step. Since the ultimate aim of virtual screening is to discover molecules with the highest success rate in the pharmaceutical industry, various aspects of the molecules in different assays were considered by integrating ten different regression models. In conclusion, this paper contributes to pharmaceutical sciences by introducing eleven diverse scaffolds and eight approved drugs that can potentially be used as inhibitors of angiogenesis receptors, including VEGFR, FGFR, and EGFR. Considering three target receptors simultaneously is another central concept and contribution used. This concept could increase the chance of success, while reducing the possibility of resistance to these agents.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3637-3659"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141085","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":"PPARγ modulator predictor (PGMP_v1): chemical space exploration and computational insights for enhanced type 2 diabetes mellitus management.","authors":"Sk Abdul Amin, Lucia Sessa, Shovanlal Gayen, Stefano Piotto","doi":"10.1007/s11030-025-11118-5","DOIUrl":"10.1007/s11030-025-11118-5","url":null,"abstract":"<p><p>Peroxisome proliferator-activated receptor gamma (PPARγ) plays a critical role in adipocyte differentiation and enhances insulin sensitivity. In contemporary drug discovery, in silico design strategies offer significant advantages by revealing essential structural insights for lead optimization. The study is guided by two main objectives: (i) a ligand-based approach to explore the chemical space of PPARγ modulators followed by molecular docking ensembles (MDEs) to investigate ligand-binding interactions, (ii) the development of a supervised ML model for a large dataset of compounds targeting PPARγ. Additionally, the combination of chemical space networks with ML models enables the rapid screening and prediction of PPARγ modulators. These modeling analyses will assist medicinal chemists in designing more potent PPARγ modulators. To further enhance accessibility for the scientific community, we developed an online tool, \"PGMP_v1,\" aimed at prospective screening for PPARγ modulators. The tool \"PGMP_v1\" is available at the provided link https://github.com/Amincheminfom/PGMP_v1 . The integration of these computational methods has uncovered crucial structural motifs that are essential for PPARγ activity, advancing the development of more effective modulators in the future.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3305-3321"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073424","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":"Classification models and SAR analysis of anaplastic lymphoma kinase (ALK) inhibitors using machine learning algorithms with two data division methods.","authors":"Dan Qu, Aixia Yan","doi":"10.1007/s11030-024-10990-x","DOIUrl":"10.1007/s11030-024-10990-x","url":null,"abstract":"<p><p>Anaplastic lymphoma kinase (ALK) plays a critical role in the development of various cancers. In this study, the dataset of 1810 collected inhibitors were divided into a training set and a test set by the self-organizing map (SOM) and random method, respectively. We developed 32 classification models using Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) to distinguish between highly and weakly active ALK inhibitors, with the inhibitors represented by MACCS and ECFP4 fingerprints. Model 7D which was built by the RF algorithm using training set 1/test set 1 divided by the SOM method, provided the best performance with a prediction accuracy of 90.97% and a Matthews correlation coefficient (MCC) value of 0.79 on the test set. We clustered the 1810 inhibitors into 10 subsets by K-Means algorithm to find out the structural characteristics of highly active ALK inhibitors. The main scaffolds of highly active ALK inhibitors were also analyzed based on ECFP4 fingerprints. It was found that some substructures have a significant effect on high activity, such as 2,4-diarylaminopyrimidine analogues, pyrrolo[2,1-f][1,2,4]triazin, indolo[2,3-b]quinoline-11-one, benzo[d]imidazol and pyrrolo[2,3-b]pyridine. In addition, the subsets were summarized into several clusters, among which four clusters showed a significant relationship with ALK inhibitory activity. Finally, Shapley additive explanations (SHAP) was also used to explain the influence of modeling features on model prediction results. The SHAP results indicated that our models can well reflect the structural features of ALK inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"2919-2943"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142611847","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":"A multitask interpretable model with graph attention mechanism for activity prediction of low-data PIM inhibitors.","authors":"Zixiao Wang, Lili Sun, Yu Chang, Fang Yang, Kai Jiang","doi":"10.1007/s11030-024-11060-y","DOIUrl":"10.1007/s11030-024-11060-y","url":null,"abstract":"<p><p>The aberrant expression of proviral integration site for Moloney murine leukemia virus (PIM) kinases is closely related to various tumors and chemotherapy resistance, making them attractive targets for cancer therapy. However, due to the extremely high homology among the three PIM isoforms (PIM1, PIM2, PIM3) and the limited availability of existing bioactivity data, screening and designing selective PIM inhibitors remain a daunting challenge. To address this issue, this study constructed a multitask regression model that can simultaneously predict the half-maximal inhibitory concentration (IC₅₀ values). The model utilizes an attention mechanism to capture effects within local atomic groups and the interactions between different groups of atoms. Through weight sharing, the model enhances the accuracy of predicting PIM3 inhibitors by leveraging the rich and highly correlated data from PIM1 and PIM2 isoforms. Additionally, visualizing the weights of nodes (atoms in the molecule) in the model helps us to intuitively understand the relationship between molecular features and prediction outcomes, thereby enhancing the interpretability of the model. In summary, this work provides new insights and methods for performing activity prediction tasks for multiple similar targets in low-data scenarios.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3101-3112"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142765266","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 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":"<p><p>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.</p>","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}

{"title":"First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.","authors":"Rinki Prasad Bhagat, Jyotisha, Indrasis Dasgupta, Sk Abdul Amin, Pranay Jakkula, Arijit Bhattacharya, Insaf Ahmed Qureshi, Shovanlal Gayen","doi":"10.1007/s11030-025-11217-3","DOIUrl":"10.1007/s11030-025-11217-3","url":null,"abstract":"<p><p>In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 inhibitors is quite a challenge for the chemist community due to the unavailability of the crystal structure of HDAC11. Ligand-based drug design (LBDD) strategies are the hope to speed up the development of its inhibitors. Here, an in-depth analysis of 712 HDAC11 inhibitors is performed through compound space networks and various cheminformatics approaches. The analyses demonstrated significant clustering of similar compounds based on their chemical structures, offering valuable insights into the chemical space occupied by HDAC11 inhibitors. Furthermore, the current work aimed to develop robust classification-based QSAR models that deliver the essential structural fingerprints. This study highlighted that the compounds bearing scaffolds such as isoindoline, benzimidazole, carboxamide/hydroxamate moieties, etc., are important for HDAC11 inhibitors. Molecular docking and MD simulations further provide an in-depth analysis of the binding interaction of the identified fingerprints in the catalytic site of HDAC11. In brief, our study delivers some important structural attributes that will aid medicinal chemists in designing and developing future potent HDAC11 inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3679-3702"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085631","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}