SAR and QSAR in Environmental Research最新文献_第2页

Read-across-driven binary classification for the developmental and reproductive toxicity of organic compounds tested according to the OECD test guidelines 421/422. 根据经合组织测试指南421/422测试的有机化合物的发育和生殖毒性的交叉驱动二元分类。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-03-01 Epub Date: 2025-04-17 DOI: 10.1080/1062936X.2025.2483765

M Chatterjee, S Pore, Z Szepesi, K Roy

{"title":"Read-across-driven binary classification for the developmental and reproductive toxicity of organic compounds tested according to the OECD test guidelines 421/422.","authors":"M Chatterjee, S Pore, Z Szepesi, K Roy","doi":"10.1080/1062936X.2025.2483765","DOIUrl":"https://doi.org/10.1080/1062936X.2025.2483765","url":null,"abstract":"Developmental and reproductive toxicity (DART) refers to the adverse effects on sexual function, fertility, and the development of offspring resulting from exposure to toxic substances or chemicals, which may occur at various stages of the reproductive cycle. In response to the increasing volume of chemicals, regulatory bodies advocate for implementing various new approach methodologies (NAMs) as alternatives to animal testing, enabling rapid assessments of the toxic potential of numerous chemical substances. In this study, in silico methodologies were utilized to assess the DART properties of various industrial chemicals. We employed a Read-Across (RA)-based binary classification approach to evaluate the DART potential of these chemicals. The data for the binary classification have been compiled from two distinct sources: eChemPortal (https://www.echemportal.org/echemportal/) and the National Institute of Health Sciences (NIHS) databases. The information gathered from these sources encompasses two types of toxicity data: No Observed Adverse Effect Level (NOAEL) and Low Observed Adverse Effect Level (LOAEL) tested as per the Organisation for Economic Co-operation and Development Test Guidelines 421 and 422, adopting the principles of Good Laboratory Practice (GLP). The data were utilized separately for safety assessment through a binary classification-based read-across prediction, demonstrating commendable classification capabilities for new chemicals (Accuracytest ~0.700).","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 3","pages":"247-270"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting acute toxicity of pesticides towards Daphnia magna with random forest algorithm. 随机森林算法预测农药对大水蚤的急性毒性。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-03-01 Epub Date: 2025-04-14 DOI: 10.1080/1062936X.2025.2478510

S Xu

引用次数: 0

Binding mechanism of inhibitors to DFG-in and DFG-out P38α deciphered using multiple independent Gaussian accelerated molecular dynamics simulations and deep learning. 利用多重独立的高斯加速分子动力学模拟和深度学习，破解了抑制剂与DFG-in和DFG-out P38α的结合机制。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-02-01 Epub Date: 2025-03-20 DOI: 10.1080/1062936X.2025.2475407

G Xu, W Zhang, J Du, J Cong, P Wang, X Li, X Si, B Wei

{"title":"Binding mechanism of inhibitors to DFG-in and DFG-out P38α deciphered using multiple independent Gaussian accelerated molecular dynamics simulations and deep learning.","authors":"G Xu, W Zhang, J Du, J Cong, P Wang, X Li, X Si, B Wei","doi":"10.1080/1062936X.2025.2475407","DOIUrl":"10.1080/1062936X.2025.2475407","url":null,"abstract":"P38α has been identified as a key target for drug design to treat a wide range of diseases. In this study, multiple independent Gaussian accelerated molecular dynamics (GaMD) simulations, deep learning (DL), and the molecular mechanics generalized Born surface area (MM-GBSA) method were used to investigate the binding mechanism of inhibitors (SB2, SK8, and BMU) to DFG-in and DFG-out P38α and clarify the effect of conformational differences in P38α on inhibitor binding. GaMD trajectory-based DL effectively identified important functional domains, such as the A-loop and N-sheet. Post-processing analysis on GaMD trajectories showed that binding of the three inhibitors profoundly affected the structural flexibility and dynamical behaviour of P38α situated at the DFG-in and DFG-out states. The MM-GBSA calculations not only revealed that differences in the binding ability of inhibitors are affected by DFG-in and DFG-out conformations of P38α, but also confirmed that van der Waals interactions are the primary force driving inhibitor-P38α binding. Residue-based free energy estimation identifies hot spots of inhibitor-P38α binding across DFG-in and DFG-out conformations, providing potential target sites for drug design towards P38α. This work is expected to offer valuable theoretical support for the development of selective inhibitors of P38α family members.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"101-126"},"PeriodicalIF":2.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine. 新兴污染物对过氧化物酶体增殖物激活受体γ (PPARγ) IC50抑制的预测建模

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-02-01 Epub Date: 2025-03-24 DOI: 10.1080/1062936X.2025.2478123

A Awomuti, Z Yu, O Adesina, O W Samuel, A W Mumbi, D Yin

{"title":"Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine.","authors":"A Awomuti, Z Yu, O Adesina, O W Samuel, A W Mumbi, D Yin","doi":"10.1080/1062936X.2025.2478123","DOIUrl":"10.1080/1062936X.2025.2478123","url":null,"abstract":"Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays a pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces a novel approach to predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, and preservatives, on PPARγ. The predictive model, based on the light-gradient boosting machine (LightGBM) algorithm, was trained on a dataset of 1804 molecules showed r2 values of 0.82 and 0.59, Mean Absolute Error (MAE) of 0.38 and 0.58, and Root Mean Square Error (RMSE) of 0.54 and 0.76 for the training and test sets, respectively. This study provides novel insights into the interactions between emerging contaminants and PPARγ, highlighting the potential hazards and risks these chemicals may pose to public health and the environment. The ability to predict PPARγ inhibition by these hazardous contaminants demonstrates the value of this approach in guiding enhanced environmental toxicology research and risk assessment.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"145-167"},"PeriodicalIF":2.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties. 通过基于可解释 ML 的 q-RASPR 方法对药物分子的内在膜渗透性进行建模，以获得更好的药代动力学和毒代动力学特性。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-02-01 Epub Date: 2025-04-07 DOI: 10.1080/1062936X.2025.2478118

I Dasgupta, H Barik, S Gayen

{"title":"Modelling of intrinsic membrane permeability of drug molecules by explainable ML-based q-RASPR approach towards better pharmacokinetics and toxicokinetics properties.","authors":"I Dasgupta, H Barik, S Gayen","doi":"10.1080/1062936X.2025.2478118","DOIUrl":"10.1080/1062936X.2025.2478118","url":null,"abstract":"Drug discovery's success lies in potent inhibition against a target and optimum pharmacokinetic and toxicokinetic properties of drug molecules. Membrane permeability is a crucial factor in determining the absorption, distribution, metabolism, and excretion of drug molecules, thereby determining the pharmacokinetic and toxicokinetic properties important for drug development. Intrinsic permeability (P0) is more crucial than apparent permeability (Papp) in assessing the transport of drug molecules across a membrane. It gives more consistent results due to its non-dependency on external/site-specific factors. In the present work, our focus is on the construction of a machine learning (ML)-based quantitative read-across structure-property relationship (q-RASPR) model of intrinsic permeability of drug molecules by utilizing both linear and non-linear algorithms. The Support Vector Regression (SVR) q-RASPR model was found to be the best model having superior predictive ability (Q2F1 = 0.788, Q2F2 = 0.785, MAEtest = 0.637). The contribution of important descriptors in the final model is explained to get a mechanistic interpretation of intrinsic permeability. Overall, the present study unveils the application of the q-RASPR framework for significant improvement of the external predictivity of the traditional QSPR model in the case of intrinsic permeability to get a better assessment of the total permeability of drug molecules.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 2","pages":"127-143"},"PeriodicalIF":2.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning. 利用多特征选择和机器学习增强基于硅qsar的丁基胆碱酯酶抑制剂筛选。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-02-01 Epub Date: 2025-02-21 DOI: 10.1080/1062936X.2025.2466020

D Sharmistha, M Prabha, R R Siva Kiran, H Ashoka

{"title":"Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning.","authors":"D Sharmistha, M Prabha, R R Siva Kiran, H Ashoka","doi":"10.1080/1062936X.2025.2466020","DOIUrl":"10.1080/1062936X.2025.2466020","url":null,"abstract":"Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer's disease. We developed a QSAR model to facilitate the identification of effective butyrylcholinesterase inhibitors. The model employs multi-feature selection and feature learning, improving the in silico screening efficiency and accelerating drug discovery efforts. This study aims to integrate Human Intestinal Absorption (HIA) values of butyrylcholinesterase (BChE) target inhibitors and their 50% inhibitory concentration (IC50) with machine learning tools. The model was developed using chemical descriptors in combination with supervised machine learning classification algorithms. Random Forest Classifier algorithm proved to be the ultimate best fit for classification model metrics including log loss probability (0.04225), accuracy score (98.88%) and Matthew's correlation coefficient (0.98). Furthermore, a subset of the active dataset was used to study the regression based on HIA values using multi-feature selection and feature learning. The models were validated using precision, recall and F1 score for regression modelling. After integrating HIA data with existing machine learning algorithms, we observed a significant reduction of 89.63% in the number of inhibitors. The findings provide valuable pharmacological insights that can help in future design of drug development schemes different from conventional methods.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"79-99"},"PeriodicalIF":2.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Molecular mechanism of interactions of SPIN1 with novel inhibitors through molecular docking and molecular dynamics simulations. 通过分子对接和分子动力学模拟SPIN1与新型抑制剂相互作用的分子机制。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-01-01 Epub Date: 2025-02-24 DOI: 10.1080/1062936X.2025.2463586

S Wang, R Wang, J Yang, L Xu, B Zhao, L Chen

{"title":"Molecular mechanism of interactions of SPIN1 with novel inhibitors through molecular docking and molecular dynamics simulations.","authors":"S Wang, R Wang, J Yang, L Xu, B Zhao, L Chen","doi":"10.1080/1062936X.2025.2463586","DOIUrl":"10.1080/1062936X.2025.2463586","url":null,"abstract":"Methyllysine reading protein Spindlin 1 (SPIN1) plays a crucial role in histone post-translational modifications and serves as an effective target for the treatment of various malignant tumours. Although several inhibitors targeting SPIN1 expression have been identified, the atomic-level interactions between SPIN1 and inhibitors remain unclear. In this study, six potential SPIN1 inhibitors A366, EML631, MS31, MS8535, vinspinln, and XY49-92B were selected for molecular docking with SPIN1. Conformational changes in SPIN1 induced by these inhibitors, as well as their interactions, were investigated using molecular dynamics simulation (MD) and energy prediction methods including molecular mechanics generalized Born surface area (MM-GBSA) and solvation interaction energy (SIE). The findings indicate that the binding pockets within domain II, specifically Phe141, Trp151, Tyr170, and Tyr177, engage in cation-π interactions with these inhibitors, while also contributing to van der Waals hydrophobic interactions of varying strengths. These van der Waals hydrophobic interactions are critical for their binding affinity, while electrostatic interactions are significantly counterbalanced by polar solvation effects. In addition, through virtual screening and molecular docking, a new lead compound CXY49 was found presenting an effective binding to SPIN1. The structural and energetic changes identified in this study provide valuable insights for the development of new SPIN1 inhibitors.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"36 1","pages":"57-77"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143483972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of monomer structures and fragments of local symmetry for simulation of glass transition temperatures of polymers. 单体结构和局部对称片段在聚合物玻璃化转变温度模拟中的应用。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-01-01 Epub Date: 2025-01-29 DOI: 10.1080/1062936X.2025.2453868

A P Toropova, A A Toropov, V O Kudyshkin, D Leszczynska, J Leszczynski

引用次数: 0

First report on q-RASTR modelling of hazardous dose (HD₅) for acute toxicity of pesticides: an efficient and reliable approach towards safeguarding the sensitive avian species. 关于q-RASTR农药急性毒性危险剂量（HD5）模型的第一份报告：保护敏感鸟类物种的有效和可靠方法。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-01-01 Epub Date: 2025-02-11 DOI: 10.1080/1062936X.2025.2462559

S Das, A Bhattacharjee, P K Ojha

{"title":"First report on q-RASTR modelling of hazardous dose (HD5) for acute toxicity of pesticides: an efficient and reliable approach towards safeguarding the sensitive avian species.","authors":"S Das, A Bhattacharjee, P K Ojha","doi":"10.1080/1062936X.2025.2462559","DOIUrl":"10.1080/1062936X.2025.2462559","url":null,"abstract":"Pesticides are crucial in modern agriculture, significantly enhancing crop productivity by managing pests. It is important to evaluate their toxicity to minimize health risks to bird species and preserve ecosystem balance. Traditional parameters including lethal concentration (LC50) or median lethal dose (LD50) often underestimate hazards due to limited data and uncertainty about the most sensitive species tested. This limitation can be addressed using extrapolation factors like HD5 accounting for 50% mortality of the most sensitive 5% of bird species. In this research, a QSTR model was developed utilizing a diverse set of 480 pesticides using partial least squares (PLS) regression with 2D descriptors. Additionally, a PLS-based quantitative read-across structure-toxicity relationship (q-RASTR) and classification based models were constructed. The q-RASTR model outperformed traditional QSTR approaches, achieving robust statistical performance with internal validation metrics r2 = 0.623, Q2 = 0.569 and external validation metrics Q2F1 = 0.541, Q2F2 = 0.540. Key factors influencing avian toxicity were identified. The q-RASTR model was used to screen the Pesticide Properties Database (PPDB) to recognize the most and least toxic pesticides for avian species, aligning well with real-world data. This work provides a more economical and ethical alternative to conventional in vivo testing methods, aiding regulatory bodies and industries in developing safer, environmentally friendly pesticides.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"39-55"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Targeting human arginyltransferase and post-translational protein arginylation: a pharmacophore-based multilayer screening and molecular dynamics approach to discover novel inhibitors with therapeutic promise. 靶向人精氨酸转移酶和翻译后蛋白精氨酸化：基于药物团的多层筛选和分子动力学方法发现具有治疗前景的新型抑制剂。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2025-01-01 Epub Date: 2025-01-23 DOI: 10.1080/1062936X.2025.2452001

R Naga, S Poddar, A Jana, S Maity, P Kar, D R Banerjee, S Saha

{"title":"Targeting human arginyltransferase and post-translational protein arginylation: a pharmacophore-based multilayer screening and molecular dynamics approach to discover novel inhibitors with therapeutic promise.","authors":"R Naga, S Poddar, A Jana, S Maity, P Kar, D R Banerjee, S Saha","doi":"10.1080/1062936X.2025.2452001","DOIUrl":"10.1080/1062936X.2025.2452001","url":null,"abstract":"Protein arginylation mediated by arginyltransferase 1 is a crucial regulator of cellular processes in eukaryotes by affecting protein stability, function, and interaction with other macromolecules. This enzyme and its targets are of immense interest for modulating cellular processes in diseased states like obesity and cancer. Despite being an important target molecule, no highly potent drug against this enzyme exists. Therefore, this study focuses on discovering potential inhibitors of human arginyltransferase 1 by computational approaches where screening of over 300,000 compounds from natural and synthetic databases was done using a pharmacophore model based on common features among known inhibitors. The drug-like properties and potential toxicity of the compounds were also assessed in the study to ensure safety and effectiveness. Advanced methods, including molecular simulations and binding free energy calculations, were performed to evaluate the stability and binding efficacy of the most promising candidates. Ultimately, three compounds were identified as potent inhibitors, offering new avenues for developing therapies targeting arginyltransferase 1.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"1-28"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0