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

Mapping toxicity pathways of per- and polyfluoroalkyl substances using interpretable classification-based machine learning models. 使用可解释的基于分类的机器学习模型绘制全氟烷基和多氟烷基物质的毒性途径。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-07-01 Epub Date: 2026-06-29 DOI: 10.1080/1062936X.2026.2693300

S Sarkar, S Pore, K Roy

{"title":"Mapping toxicity pathways of per- and polyfluoroalkyl substances using interpretable classification-based machine learning models.","authors":"S Sarkar, S Pore, K Roy","doi":"10.1080/1062936X.2026.2693300","DOIUrl":"10.1080/1062936X.2026.2693300","url":null,"abstract":"Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants associated with adverse human health outcomes. However, experimental toxicity data remain unavailable for the vast majority of PFAS, limiting comprehensive risk assessment. In this study, ML-based classification Quantitative Structure Toxicity Relationship (QSTR) models were developed to predict PFAS toxicity across three biologically relevant high-throughput screening endpoints. These included AID-1030 (ALDH1A1 inhibition associated with reproductive toxicity), AID-504444 (Nrf2 pathway inhibition responsible for vascular disruption, hepatic steatosis, lung carcinogenesis, and infertility), and AID-588855 (inhibition of TGF-β/Smad3 signalling linked to developmental toxicity and tumour progression). To address pronounced class imbalance in these datasets, multiple data balancing techniques (ADASYN, SMOTE, Borderline-SMOTE, SVMSMOTE, and random oversampling) were applied. Fourteen ML classifiers were trained for each balanced dataset, yielding 70 models per endpoint. Sum-of-Ranking-Differences (SRD) analysis identified the most robust models with Gradient Boosting, Random Forest, and Support Vector Classifier models emerging as optimal for AID-1030, AID-504444, and AID-588855, respectively. SHAP and substructure analyses provided mechanistic interpretability, by linking PFAS structural features and AOP progression. The optimized models were further applied to an independent external dataset of 2,361 PFAS, and a Python-based screening tool, PERSIST, was developed to screen PFAS.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"559-592"},"PeriodicalIF":2.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346123","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

An integrated deep learning and molecular dynamics pipeline for the discovery of novel CDK8 inhibitors against acute myeloid leukaemia. 一个集成的深度学习和分子动力学管道，发现新的CDK8抑制剂对抗急性髓性白血病。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-07-01 Epub Date: 2026-08-07 DOI: 10.1080/1062936X.2026.2709353

B R Awad, M Sargolzaei, H Nikoofard

{"title":"An integrated deep learning and molecular dynamics pipeline for the discovery of novel CDK8 inhibitors against acute myeloid leukaemia.","authors":"B R Awad, M Sargolzaei, H Nikoofard","doi":"10.1080/1062936X.2026.2709353","DOIUrl":"10.1080/1062936X.2026.2709353","url":null,"abstract":"Cyclin-dependent kinase 8 (CDK8) has emerged as a promising therapeutic target for acute myeloid leukaemia (AML). We developed an integrated computational pipeline combining neural network-based potency prediction with molecular dynamics (MD) simulations for CDK8 inhibitor discovery. A curated dataset of 1,200 unique CDK8 inhibitors was assembled from ChEMBL. The optimal neural network architecture (two hidden layers, 512→128 units) with dropout regularization achieved a test set r2 of 0.47 and RMSE of 0.87. Y-randomization testing confirmed genuine structure-activity relationships. Using an extrapolation-focused training strategy combined with a SELFIES-based genetic recombination algorithm, we generated novel molecular structures beyond the training distribution. SHAP analysis revealed fingerprint bits as critical determinants of potency. Applicability‑domain analysis confirmed that the novel hit falls within validated chemical space. The identified candidate exhibited a predicted pIC50 of 11.97, substantially exceeding the most potent training compound (pIC50 = 10.09) and compound 12 (pIC50 = 7.47). Molecular docking revealed Moldock scores of -143.9 kcal/mol for the novel hit versus -113.9 kcal/mol for compound 12. MD simulations demonstrated stable binding with the novel hit forming a highly stable hydrogen bond with Asp98. MM-GBSA calculations showed superior binding free energy for the novel hit (-99.10 vs. -47.77 kcal/mol).","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"619-653"},"PeriodicalIF":2.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148685746","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

When the founding editor takes his final bow. 当创始编辑最后一次鞠躬。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-07-01 Epub Date: 2026-09-15 DOI: 10.1080/1062936X.2026.2696713

J Devillers

引用次数: 0

Multiscale computational evaluation of marine fungal metabolites containing iminohydantoin-like scaffolds as anti-Alzheimer drug candidates. 含亚胺酰脲样支架的海洋真菌代谢物作为抗阿尔茨海默病候选药物的多尺度计算评价。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-06-01 Epub Date: 2026-06-24 DOI: 10.1080/1062936X.2026.2685535

A I Foudah, A Ahmad, M A Bakht, T Jawaid, F Alotaibi, Z Rehman, M Imran, A Alam

{"title":"Multiscale computational evaluation of marine fungal metabolites containing iminohydantoin-like scaffolds as anti-Alzheimer drug candidates.","authors":"A I Foudah, A Ahmad, M A Bakht, T Jawaid, F Alotaibi, Z Rehman, M Imran, A Alam","doi":"10.1080/1062936X.2026.2685535","DOIUrl":"10.1080/1062936X.2026.2685535","url":null,"abstract":"Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with amyloid-beta accumulation, neuroinflammation, and synaptic dysfunction, making beta-secretase 1 (BACE1) a promising therapeutic target. In the current investigation, we used an integrated computational approach, including virtual screening, machine learning, quantum-chemical analysis, molecular dynamics simulations, ADMET profiling, and network pharmacology, to identify marine fungal metabolites with potential BACE1-binding activity. A virtual chemical library consisting of 4,683 marine-derived compounds was virtually screened against the BACE1 catalytic subdomain to select 100 top compounds with docking energy ranging from -11.9 to -10.2 kcal/mol. The pharmacokinetic study showed promising CNS characteristics. CatBoost was identified as the most reliable model (r2 = 0.243 ± 0.049), predicting pIC50 values of 7.51-7.95 for shortlisted marine fungal metabolites, suggesting potential BACE1 inhibitory activity. Density functional theory computations revealed good ligand reactivity and stability. Molecular dynamics simulation validated the stable ligand-protein interactions, and MM/GBSA analysis selected CMNPD7259 (ΔG_bind = -68.45 kcal/mol) as the most promising molecule. Network pharmacology further associated the metabolites with pathways involved in inflammation, apoptosis, immunological control, and neuronal function. Overall, the results indicate that metabolites from marine fungi are intriguing candidates for developing safer, more efficacious treatments for AD.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"37 6","pages":"469-498"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309232","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

Structure-based identification of inhibitory compounds targeting M32 metallocarboxypeptidase of Leishmania donovani. 多诺瓦利什曼原虫M32金属羧肽酶抑制化合物的结构鉴定。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-06-01 Epub Date: 2026-06-24 DOI: 10.1080/1062936X.2026.2686780

S Sen, P Jakkula, I A Qureshi

{"title":"Structure-based identification of inhibitory compounds targeting M32 metallocarboxypeptidase of Leishmania donovani.","authors":"S Sen, P Jakkula, I A Qureshi","doi":"10.1080/1062936X.2026.2686780","DOIUrl":"10.1080/1062936X.2026.2686780","url":null,"abstract":"Leishmaniasis remains a major public health burden due to limited vaccines, toxic treatments, and drug resistance. The M32 metallocarboxypeptidase of Leishmania donovani (LdMCP1) represents a promising target due to its presence in trypanosomatids and absence in mammals. In this study, an in silico strategy was applied to predict the three-dimensional structure of LdMCP1, revealing a canonical α/β topology and the characteristic catalytic motifs of the M32 family. Dimeric interface analysis indicated that the N-terminal subdomain plays a critical role in inter-subunit interactions and overall dimer stability. Molecular docking delineated the binding mode of a peptide substrate within the active site, highlighting key interactions with catalytic residues. Virtual screening of GSK anti-kinetoplastid libraries identified three compounds as lead molecules based on docking scores, substrate interaction patterns, and previously reported in vitro inhibitory activity against TcMCP1. These compounds exhibited favourable physicochemical properties, ADMET and pharmacokinetic profiles. All protein-ligand complexes demonstrated structural integrity, compactness, and flexibility during the molecular dynamics simulations. MM-PBSA binding free energy calculations indicated TCMDC-143515 and TCMDC-143620 bind more strongly than the substrate. Collectively, these findings suggest that LdMCP1 is a druggable enzyme and identified lead molecules provide a rational starting point for the design and optimization of specific antileishmanial agents.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"37 6","pages":"533-556"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309255","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

Multi-target QSAR modelling for identification of novel inhibitors of class I HDACs. 多靶点QSAR模型用于鉴定I类hdac的新型抑制剂。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-06-01 Epub Date: 2026-05-28 DOI: 10.1080/1062936X.2026.2676682

G G Tu

{"title":"Multi-target QSAR modelling for identification of novel inhibitors of class I HDACs.","authors":"G G Tu","doi":"10.1080/1062936X.2026.2676682","DOIUrl":"10.1080/1062936X.2026.2676682","url":null,"abstract":"In this study, the multi-target QSAR (mt-QSAR) models were constructed which can predict the inhibitory activity of compounds against various class I HDACs isoforms under different experimental conditions. Models based on mt-QSAR classification (a linear model and six non-linear models) were constructed using 1215 compounds obtained from the ChEMBL database by the Box - Jenkins moving average method using 13 deviation descriptors. The high predictive performance was found in the non-linear models based on Support Vector Classification, Random Forest and Gradient Boosting with accuracies exceeding 90% for the sub-training, test and validation sets. Additionally, virtual screening was performed using the ZINC library to identify a potential hit compound. The SwissADME was used for in silico predictions to assess drug-likeness of the identified virtual hit. Finally, docking and molecular dynamics simulations were performed to study the interactions of target proteins with the hit compound. It was found that coordination of the ligand with the catalytic zinc ion is essential for inhibitory activity. The results offer important insights for the search for new inhibitors of class I HDACs as potential therapeutic agents.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"449-467"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148043551","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

Conformational landscapes and binding free energies of multitarget phytochemicals reveal molecular recognition mechanisms in colorectal cancer-associated proteins. 多靶点植物化学物质的构象景观和结合自由能揭示结直肠癌相关蛋白的分子识别机制。

IF 2.8 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-06-01 Epub Date: 2026-06-24 DOI: 10.1080/1062936X.2026.2685532

H Deshmukh, S Samanta, J R Kanwar, M Verma, R Chowdhary, P Kar

{"title":"Conformational landscapes and binding free energies of multitarget phytochemicals reveal molecular recognition mechanisms in colorectal cancer-associated proteins.","authors":"H Deshmukh, S Samanta, J R Kanwar, M Verma, R Chowdhary, P Kar","doi":"10.1080/1062936X.2026.2685532","DOIUrl":"10.1080/1062936X.2026.2685532","url":null,"abstract":"Colorectal cancer (CRC) significantly contributes to global cancer-related morbidity and mortality. As systemic toxicity, poor selectivity, and drug resistance restrict the usefulness of available chemotherapy and radiotherapy, phytochemicals present a promising substitute strategy with their generally low toxicity and potential for multitarget action. In this study, key proteins involved in CRC signalling were selected as therapeutic targets, and baicalin, berberine, luteolin, quercetin, and licorice glycoside D1 (licorice) were evaluated as potential multitarget therapeutics. Molecular docking revealed key stabilizing interactions, such as hydrogen bonds, π-π stacking, and π-cation contacts. Additionally, eight complexes investigated via 200 ns all-atom molecular dynamics simulations consistently showed stability in the protein RMSD profiles. MM/PBSA binding free energy calculations identified the BCL-2-baicalin, IL-1β-baicalin, IL-1β-licorice, and TNF-α-licorice complexes (ΔGbind of -27.66, -26.47, -29.69, and -29.87 kcal/mol, respectively) as exhibiting the strongest protein-ligand affinities, where strong van der Waals interactions effectively offset opposing polar contributions. Free-energy surface analyses revealed prominent protein conformations as well as ligand conformations that varied depending on the bound protein. Overall, insights from our computational analyses identified baicalin and licorice as promising multitarget inhibitors while also providing energetic and mechanistic understanding that can be leveraged in future rational drug design.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"37 6","pages":"499-532"},"PeriodicalIF":2.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309258","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

QSAR-based evaluation of plant-derived larvicidal agents against Zika vector Aedes aegypti via Monte Carlo optimization and SMILES-based descriptors and molecular docking. 基于蒙特卡罗优化、smiles描述符和分子对接的qsar评价植物源性灭蚊剂对Zika媒介埃及伊蚊的杀灭效果

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-05-01 Epub Date: 2026-05-28 DOI: 10.1080/1062936X.2026.2675527

S Lotfi, F Nooraei, S Ahmadi

{"title":"QSAR-based evaluation of plant-derived larvicidal agents against Zika vector Aedes aegypti via Monte Carlo optimization and SMILES-based descriptors and molecular docking.","authors":"S Lotfi, F Nooraei, S Ahmadi","doi":"10.1080/1062936X.2026.2675527","DOIUrl":"10.1080/1062936X.2026.2675527","url":null,"abstract":"Aedes aegypti is the principal vector responsible for the transmission of several arboviral diseases, including dengue, Zika, chikungunya, and yellow fever, posing a significant threat to global public health. Controlling its population is crucial to limiting the spread of these infections. Among the most effective strategies is the use of larvicides derived from plant sources, which interfere with the mosquito's development and reduce its proliferation. This study presents a QSAR analysis of 255 plant-based larvicidal compounds against Aedes aegypti (Zika vector), using pLC50 values as the measure of bioactivity. QSAR models were developed using the CORAL software, which applies a Monte Carlo optimization algorithm to determine the correlation weights of molecular descriptors. The predictive power and reliability of the models were extensively validated using several statistical indicators. Results demonstrated that the models were robust, simple, and predictive, with validation metrics including r2, Q2, IIC, and CII ranging from 0.6877 to 0.7820, 0.6622 to 0.7643, 0.5638 to 0.8324, and 0.8248 to 0.8548, respectively. Based on these models, important molecular descriptors influencing the enhancement or reduction of larvicidal activity were determined. Furthermore, molecular docking studies were performed to investigate the binding interactions and conformational poses of the selected compounds.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"423-447"},"PeriodicalIF":2.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148043535","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

AI-driven QSAR modelling and virtual screening in the discovery of selective dopamine D₂ receptor ligands. 人工智能驱动的QSAR建模和虚拟筛选在选择性多巴胺D2受体配体发现中的应用。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-05-01 Epub Date: 2026-06-09 DOI: 10.1080/1062936X.2026.2669808

N Maliyakkal, H C Vishwakarma, S Kumar, S Kattil Parmbil, K Neha, B Mathew

{"title":"AI-driven QSAR modelling and virtual screening in the discovery of selective dopamine D2 receptor ligands.","authors":"N Maliyakkal, H C Vishwakarma, S Kumar, S Kattil Parmbil, K Neha, B Mathew","doi":"10.1080/1062936X.2026.2669808","DOIUrl":"10.1080/1062936X.2026.2669808","url":null,"abstract":"The dopamine D2 receptor (DRD2) is a key therapeutic target for several neuropsychiatric disorders, driving the need for new ligands with improved safety and efficacy. To find possible DRD2 inhibitors, we developed an integrated in silico workflow in this study that combines drug-likeness filtering, machine learning-based quantitative structure-activity relationship (ML-QSAR) modelling, and structure-based virtual screening. A standardized dataset of 1,128 DRD2 ligands with experimental inhibition constants was assembled, using pKi50 as the activity metric. Regression-based ML-QSAR models were constructed using the PubChem database and Substructure fingerprints. Random Forest techniques demonstrated the best prediction performance and robustness among these models. Crucial DRD2-binding motifs included aromatic systems, heterocycles, alkyl-aryl ethers, and halogenated groups. Strong agreement between predicted and experimental pKi50 values for FDA-approved antipsychotic medications further confirmed the validity of the model. A CNS-targeted chemical library was subjected to virtual screening, and the lead compounds were evaluated using molecular docking against the crystal structure of the dopamine D2 receptor (DRD2). VS012-7128 demonstrated strong binding affinities and formed essential interactions inside the receptor binding pocket in the molecular dynamics simulation. The study documented the effectiveness and reliability of the employed computational approach for identifying potential DRD2 ligands.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"401-422"},"PeriodicalIF":2.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148205946","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

Ionic liquids as emerging environmental contaminants: a critical review of fate, toxicity mechanisms, and sustainable design strategies. 离子液体作为新兴的环境污染物：对命运、毒性机制和可持续设计策略的重要回顾。

IF 2.3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2026-05-01 Epub Date: 2026-05-18 DOI: 10.1080/1062936X.2026.2667959

D Wang, H Ren, H Liu

{"title":"Ionic liquids as emerging environmental contaminants: a critical review of fate, toxicity mechanisms, and sustainable design strategies.","authors":"D Wang, H Ren, H Liu","doi":"10.1080/1062936X.2026.2667959","DOIUrl":"10.1080/1062936X.2026.2667959","url":null,"abstract":"Ionic liquids (ILs) are increasingly detected in various environments, raising profound concerns regarding their ecological impacts and long-term sustainability. While their tunability offers immense application potential, it concurrently introduces complex and unpredictable environmental risks that are not yet fully understood. This review provides a comprehensive synthesis and analysis of the multifaceted environmental footprint of ILs. We systematically elucidate the fundamental molecular mechanisms underpinning their ecotoxicity, framing IL ions as exogenous ligands that disrupt biological systems through membrane permeabilization, coordination-driven inhibition of metalloenzymes, and intercalation with genetic material. A particular focus is placed on tracing the environmental fate and behaviour of ILs. Furthermore, the review critically evaluates the burgeoning role of quantitative structure-toxicity relationship (QSTR) models and machine learning in advancing from retrospective hazard assessment to prospective risk prediction. By integrating mechanistic insights with fate analysis and computational toxicology, we consolidate a forward-looking framework for the rational design of greener ILs. The review concludes by identifying critical knowledge gaps - such as the implications of long-term low-dose exposure, mixture toxicity effects, and the environmental impact of transformation products - and proposes essential research directions to steer the sustainable development and regulated application of this versatile class of chemicals.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"349-372"},"PeriodicalIF":2.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964462","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