化学最新文献

{"title":"Quantification of poly(ethylene terephthalate) in environmental samples after methanolysis via gas chromatography-mass spectrometry.","authors":"Tim Lauschke, Ann-Christin Merfels, Thomas A Ternes, Georg Dierkes","doi":"10.1007/s00216-025-05963-4","DOIUrl":"10.1007/s00216-025-05963-4","url":null,"abstract":"<p><p>Quantification of the polyester poly(ethylene terephthalate) (PET) in environmental samples is a particular challenge. Due to strong matrix effects by inorganic compounds, thermoanalytical methods are not recommendable for a precise quantification of PET in complex environmental matrices. It was shown that depolymerization followed by determination of chemolysis products is a good alternative. In this study, we developed a quantification method for PET based on methanolysis to terephthalic acid dimethyl ester using sodium methoxide as a catalyst, and subsequent determination via GC-MS. With poly(ethylene terephthalate-d₄), we introduce a new internal standard covering the whole analytical process. Satisfactory detection and quantifications limits (1 and 4 µg g^-1) as well as recoveries of 87-117% were achieved. Tests with various PET-free natural compounds exhibited no interfering matrix effects. The newly developed method was applied for MP quantification in a variety of environmental samples such as sediments, sewage sludge, indoor dust, and water. In all these matrices, PET was present. Highest concentrations were detected in indoor dust with up to 57 mg/g. In bottled water, PET concentrations were detected as high as 463 ng/L. The described depolymerization method offers a straightforward approach for a reliable quantification of PET in complex environmental matrices suitable for routine analysis.</p>","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":"4461-4468"},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590154","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":"Uncertainty factors and relative response factors: correcting detection and quantitation bias in extractables and leachables studies.","authors":"Marco Giulio Rozio, Davide Angelini, Simone Carrara","doi":"10.1007/s00216-025-05946-5","DOIUrl":"10.1007/s00216-025-05946-5","url":null,"abstract":"<p><p>The transfer of chemicals from packaging or medical devices to drug formulations, known as extractables and leachables (E&L) release, can affect drug strength and safety. These released substances must be monitored and assessed through toxicological evaluation. Identifying and quantifying analytes above a specific analytical evaluation threshold (AET) is crucial, but variability in response factors (RFs) complicates accurate detection, leading to potential errors in quantitation. An uncertainty factor (UF) can partially correct this, though it is limited by RF variability, and a multidetector approach improves characterization but does not fully resolve quantitation bias. The RRFlow model proposed in this study offers a solution by determining E&L concentrations without real-time reference standards analysis. It involves identity confirmation, RRF validation, and applies an average corrective factor (RRFi). A numerical simulation benchmark (NSB) is used to compare different scenarios, such as varying UF values, RRFlow application, and fixed rescaling factors. The benchmark assigns concentration values to model compounds with different response factors, iterating the process to evaluate the number of false positive and negative errors. The numerical simulations show that RRFlow reduces detection bias and outperforms UF-based methods, mitigating false positives and negatives.</p>","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":"4331-4349"},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635872","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":"Integrating traditional QSAR and read-across-based regression models for predicting potential anti-leishmanial azole compounds.","authors":"Rajat Nandi, Anupama Sharma, Ananya Priya, Diwakar Kumar","doi":"10.1007/s11030-024-11070-w","DOIUrl":"10.1007/s11030-024-11070-w","url":null,"abstract":"<p><p>Leishmaniasis, a neglected tropical disease caused by various Leishmania species, poses a significant global health challenge, especially in resource-limited regions. Visceral Leishmaniasis (VL) stands out among its severe manifestations, and current drug therapies have limitations, necessitating the exploration of new, cost-effective treatments. This study utilized a comprehensive computational workflow, integrating traditional 2D-QSAR, q-RASAR, and molecular docking to identify novel anti-leishmanial compounds, with a focus on Glycyl-tRNA Synthetase (LdGlyRS) as a promising drug target. A feature selection process combining Genetic Function Approximation (GFA)-Lasso with Multiple Linear Regression (MLR) was used to characterize 99 azole compounds across ten structural classes. The baseline MLR model (MOD1), containing seven simple and interpretable 2D features, exhibited robust predictive capabilities, achieving an R²_train value of 0.82 and an R²_test value of 0.87. To further enhance prediction accuracy, three qualified single models (two MLR and one q-RASAR) were used to construct three consensus models (CMs), with CM2 (MAE_test = 0.127) demonstrating significantly higher prediction accuracy for test compounds than the MOD1. Subsequently, Support Vector Regression (SVR) and Boosting yielded 0.88 (R²_train), 0.86 (R²_test), 0.92 (R²_train), and 0.82 (R²_test), respectively. Molecular docking highlighted interactions of potent azoles within the QSAR dataset with critical residues in the LdGlyRS active site (Arg226 and Glu350), emphasizing their inhibitory potential. Furthermore, the pIC50 values of an accurate external set of 2000 azole compounds from the ZINC20 database were simultaneously predicted by CM2 + SVR + Boosting models and docked against the LdGlyRS, which identified Bazedoxifene, Talmetacin, Pyrvinium, Enzastaurin as leading FDA candidates, whereas three novel compounds with the database code ZINC000001153734, ZINC000011934652, and ZINC000009942262 displayed stable docked interactions and favourable ADMET assessments. Subsequently, Molecular Dynamics (MD) simulations for 100 ns were conducted to validate the findings further, offering enhanced insights into the stability and dynamic behaviour of the ligand-protein complexes. The integrated approach of this study underscores the efficacy of 2D-QSAR modelling. It identifies LdGlyRS as a promising leishmaniasis target, offering a robust strategy for discovering and optimizing anti-leishmanial compounds to address the critical need for improved treatments.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3207-3231"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142799066","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 bibliometric analysis of the Cheminformatics/QSAR literature (2000-2023) for predictive modeling in data science using the SCOPUS database.","authors":"Arkaprava Banerjee, Kunal Roy, Paola Gramatica","doi":"10.1007/s11030-024-11056-8","DOIUrl":"10.1007/s11030-024-11056-8","url":null,"abstract":"<p><p>A bibliometric analysis of the Cheminformatics/QSAR articles published in the present century (2000-2023) is presented based on a SCOPUS search made in October 2024 using a given set of search criteria. The obtained results of 52,415 documents against the specific query are analyzed based on the number of documents per year, contributions of different countries and Institutes in Cheminformatics/QSAR publications, the contributions of researchers based on the number of documents, appearance in the top-cited articles, h-index, composite c-score (ns), and the newly introduced q-score. Finally, a list of the top 50 Cheminformatics/QSAR researchers is presented. An analysis is also made for the content of the top-cited articles during the period 2000-2023 in comparison to those before 2000 to capture the trend of changes in the Cheminformatics/QSAR research. The limiting factors of any bibliometric analysis are also briefly presented.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3703-3715"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142783787","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":"AI-DPAPT: a machine learning framework for predicting PROTAC activity.","authors":"Amr S Abouzied, Bahaa Alshammari, Hayam Kari, Bader Huwaimel, Saad Alqarni, Shaymaa E Kassab","doi":"10.1007/s11030-024-11011-7","DOIUrl":"10.1007/s11030-024-11011-7","url":null,"abstract":"<p><p>Proteolysis Targeting Chimeras are part of targeted protein degradation (TPD) techniques, which are significant for pharmacological and therapy development. Small-molecule interaction with the targeted protein is a complicated endeavor and a challenge to predict the proteins accurately. This study used machine learning algorithms and molecular fingerprinting techniques to build an AI-powered PROTAC Activity Prediction Tool that could predict PROTAC activity by examining chemical structures. The chemical structures of a diverse set of PROTAC drugs and their corresponding activities are selected as a dataset for training the tool. The processes used in this study included data preparation, feature extraction, and model training. Further, evaluation was done for the performance of the various classifiers, such as AdaBoost, Support Vector Machine, Random Forest, Gradient Boosting, and Multi-Layer Perceptron. The findings show that the methods selected here depict accurate PROTAC activities. All the models in this study showed an ROC curve better than 0.9, while the random forest on the test set of the AI-DPAPT had an area under the curve score of 0.97, thus showing accurate results. Furthermore, the study revealed significant insights into the molecular features that can influence the functions of the PROTAC. These findings can potentially increase the understanding of the structure-activity correlations involved in the TPD. Overall, the investigation contributes to computational drug development by introducing this platform powered by artificial intelligence that predicts the function of PROTAC. In addition, it sped up the processes of identifying and improving previously unknown medications. The AI-DPAPT platform can be accessed online using a web server at https://ai-protac.streamlit.app/ .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"2995-3007"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142455375","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 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.","authors":"Dingfang Huang, Yu Wang, Yiming Sun, Wenhao Ji, Qing Zhang, Yunya Jiang, Haodi Qiu, Haichun Liu, Tao Lu, Xian Wei, Yadong Chen, Yanmin Zhang","doi":"10.1007/s11030-024-11044-y","DOIUrl":"10.1007/s11030-024-11044-y","url":null,"abstract":"<p><p>Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction between the two, and accurate prediction of its binding affinity is essential for discovering drugs' new uses. So far, although many predictive models based on machine learning and deep learning have been reported, most of the models mainly focus on one-dimensional sequence and two-dimensional structural characteristics of proteins and ligands, but fail to deeply explore the detailed interaction information between proteins and ligand atoms in the binding pocket region of three-dimensional space. In this study, we introduced a novel 4D tensor feature to capture key interactions within the binding pocket and developed a three-dimensional convolutional neural network (CNN) model based on this feature. Using ten-fold cross-validation, we identified the optimal parameter combination and pocket size. Additionally, we employed feature engineering to extract features across multiple dimensions, including one-dimensional sequences, two-dimensional structures of the ligand and protein, and three-dimensional interaction features between them. We proposed an efficient protein-ligand binding affinity prediction model MCDTA (multi-dimensional convolutional drug-target affinity), built on a multi-dimensional convolutional neural network framework. Feature ablation experiments revealed that the 4D tensor feature had the most significant impact on model performance. MCDTA performed exceptionally well on the PDBbind v.2020 dataset, achieving an RMSE of 1.231 and a PCC of 0.823. In comparative experiments, it outperformed five other mainstream binding affinity prediction models, with an RMSE of 1.349 and a PCC of 0.795. Moreover, MCDTA demonstrated strong generalization ability and practical screening performance across multiple benchmark datasets, highlighting its reliability and accuracy in predicting protein-ligand binding affinity. The code for MCDTA is available at https://github.com/dfhuang-AI/MCDTA .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3041-3058"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875579","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":"Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 M^pro inhibitors using the electron density of ligands and 3D binding pockets: insights from molecular docking, dynamics simulation, and MM-GBSA analysis.","authors":"Annesha Chakraborty, Vignesh Krishnan, Subbiah Thamotharan","doi":"10.1007/s11030-024-11047-9","DOIUrl":"10.1007/s11030-024-11047-9","url":null,"abstract":"<p><p>Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to identify the most promising candidates for a given drug target. In this study, the SARS-CoV-2 main protease (M^pro) was selected as the drug target. Two distinct GAN algorithms were employed to generate novel small molecules. One approach utilized experimental electron density (ED-based) data of ligands for training to generate drug-like molecules, while the second approach leveraged the target binding pocket to capture spatial and bonding relationship between atoms within the binding pockets. The ED-based approach generated approximately 26,000 molecules, whereas the binding pocket-based method produced around 100 molecules. These generated molecules were subsequently ranked based on molecular docking results using the glide XP score (both flexible and rigid docking) and AutoDock Vina. To identify the most potent GAN-derived molecules, molecular docking was also performed on co-crystallized inhibitor molecules of M^pro. The six most promising molecules from these GAN approaches were further evaluated for stability, interactions, and MM-GBSA binding free energy through molecular dynamics simulations. This analysis led to the identification of four potent M^pro inhibitor molecules, all featuring a 2-benzyl-6-bromophenol scaffold. The binding free energies of these compounds were compared with those of other M^pro inhibitors, revealing that our compounds demonstrated better affinity for M^pro than some broad-spectrum protease inhibitors. The dynamic cross-correlation matrix plot indicated strongly correlated and anti-correlated regions, potentially linked to ligand binding.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3059-3075"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142754447","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":"Case Report of a Patient With Posttraumatic Perilymphatic Fistula.","authors":"Ali Koksal, Veysel Ayyildiz, Hayri Ogul, Mecit Kantarci","doi":"10.1177/01455613221131302","DOIUrl":"10.1177/01455613221131302","url":null,"abstract":"<p><p>On a perilymphatic fistula, there is an extravasation of the perilymph fluid into the middle ear cavity. Cross-sectional imaging techniques have very important role in evaluation of inner and middle ear structures and temporal bone. While thin section CT scans can show successfully pneumolabyrinth and temporal bone fracture, high-resolution 3D volumetric MRI sequences can help to demonstrate posttraumatic ear effusion and cerebrospinal fluid fistula into inner ear or middle ear.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"469-471"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33502209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Titania: an integrated tool for in silico molecular property prediction and NAM-based modeling.","authors":"Nikoletta-Maria Koutroumpa, Maria Antoniou, Dimitra-Danai Varsou, Konstantinos D Papavasileiou, Nikolaos K Sidiropoulos, Christoforos Kyprianou, Andreas Tsoumanis, Haralambos Sarimveis, Iseult Lynch, Georgia Melagraki, Antreas Afantitis","doi":"10.1007/s11030-025-11196-5","DOIUrl":"10.1007/s11030-025-11196-5","url":null,"abstract":"<p><p>Advances in drug discovery and material design rely heavily on in silico analysis of extensive compound datasets and accurate assessment of their properties and activities through computational methods. Efficient and reliable prediction of molecular properties is crucial for rational compound design in the chemical industry. To address this need, we have developed predictive models for nine key properties, including the octanol/water partition coefficient, water solubility, experimental hydration free energy in water, vapor pressure, boiling point, cytotoxicity, mutagenicity, blood-brain barrier permeability, and bioconcentration factor. These models have demonstrated high predictive accuracy and have undergone thorough validation in accordance with OECD test guidelines. The models are seamlessly integrated into the Enalos Cloud Platform through Titania ( https://enaloscloud.novamechanics.com/EnalosWebApps/titania/ ), a comprehensive web-based application designed to democratize access to advanced computational tools. Titania features an intuitive, user-friendly interface, allowing researchers, regardless of computational expertise, to easily employ models for property prediction of novel compounds. The platform enables informed decision-making and supports innovation in drug discovery and material design. We aspire for this tool to become a valuable resource for the scientific community, enhancing both the efficiency and accuracy of property and toxicity predictions.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3555-3573"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245999/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143958362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel model for predicting mortality in the management of deep neck infections.","authors":"Xiaoyao Tao, Hongting Hua, Yehai Liu","doi":"10.1177/01455613221133245","DOIUrl":"10.1177/01455613221133245","url":null,"abstract":"<p><p>ObjectivesDeep neck infections (DNIs) are a common and intractable disease encountered in ENT clinics that impose a significant medical and financial burden on affected individuals and their families. However, insufficient data are currently available for predicting outcomes in cases of DNI. The present study thus sought to develop a novel model capable of predicting treatment outcomes of DNI patients just using indicators at the visit.MethodsPatients with DNIs treated from 2010 to 2022 were included in the present study. Patient data were retrospectively collected from medical records. Risk factors associated with mortality were identified using logistic regression models. A predictive model was constructed based on odds ratios for factors calculated using a multivariate regression model.ResultsIn total, 153 patients were enrolled in the present study. Risk factors associated with mortality included age >50 years, residence in a rural area, dyspnea at visit, the involvement of multiple infected sites, serum albumin<34 g/L, renal insufficiency, mediastinitis, pulmonary infection, and septic shock. A multivariate regression model revealed that mediastinitis (OR: 7.308, <i>P</i> < 0.001), serum creatinine>95 μmol/L (OR: 23.363, <i>P</i> < 0.05), and serum albumin<34 g/L (OR: 13.837, <i>P</i> < 0.05) were independent predictors of mortality in deep neck infection patients, with serum creatinine>95 μmol/L being particularly critical to the outcomes. Diabetes was not the predictor of mortality but was associated with long-term hospitalization (<i>P</i> < 0.001).ConclusionsIn summary, the model constructed in the present study was capable of estimating the potential for poor outcomes in DNI patients before the initiation of treatment. These findings may help improve doctor-patient communication, especially for those struggling financially.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"NP550-NP557"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40660028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}