SAR and QSAR in Environmental Research最新文献_第9页

Priority list of potential endocrine-disrupting chemicals in food chemical contaminants: a docking study and in vitro/epidemiological evidence integration. 食品化学污染物中潜在内分泌干扰化学物质的优先清单：对接研究和体外/流行病学证据整合。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-10-01 Epub Date: 2023-11-03 DOI: 10.1080/1062936X.2023.2269855

J Ren, T Jin, R Li, Y Y Zhong, Y X Xuan, Y L Wang, W Yao, S L Yu, J T Yuan

{"title":"Priority list of potential endocrine-disrupting chemicals in food chemical contaminants: a docking study and in vitro/epidemiological evidence integration.","authors":"J Ren, T Jin, R Li, Y Y Zhong, Y X Xuan, Y L Wang, W Yao, S L Yu, J T Yuan","doi":"10.1080/1062936X.2023.2269855","DOIUrl":"10.1080/1062936X.2023.2269855","url":null,"abstract":"Diet is an important exposure route of endocrine-disrupting chemicals (EDCs), but many unfiltered potential EDCs remain in food. The in silico prediction of EDCs is a popular method for preliminary screening. Potential EDCs in food were screened using Endocrine Disruptome, an open-source platform for inverse docking, to predict the binding probabilities of 587 food chemical contaminants with 18 human nuclear hormone receptor (NHR) conformations. In total, 25 contaminants were bound to multiple NHRs such as oestrogen receptor α/β and androgen receptor. These 25 compounds mainly include pesticides and per- and polyfluoroalkyl substances (PFASs). The prediction results were validated with the in vitro data. The structural features and the crucial amino acid residues of the four NHRs were also validated based on previous literature. The findings indicate that the screening has good prediction efficiency. In addition, the epidemic evidence about endocrine interference of PFASs in food on children was further validated through this screening. This study provides preliminary screening results for EDCs in food and a priority list for in vitro and in vivo research.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"34 10","pages":"847-866"},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71426528","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

What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques. 给定化学物质对给定水生物种的生态毒性是多少？使用推荐系统技术预测物种和化学品之间的相互作用。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-10-01 Epub Date: 2023-09-06 DOI: 10.1080/1062936X.2023.2254225

M Viljanen, J Minnema, P N H Wassenaar, E Rorije, W Peijnenburg

{"title":"What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques.","authors":"M Viljanen, J Minnema, P N H Wassenaar, E Rorije, W Peijnenburg","doi":"10.1080/1062936X.2023.2254225","DOIUrl":"10.1080/1062936X.2023.2254225","url":null,"abstract":"Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solving this apparent contradiction as they allow to generalize toxicity patterns across chemicals and species. However, despite the availability of large public toxicity datasets, the data is highly sparse, complicating model development. The aim of this study is to provide insights into how ML can predict toxicity using a large but sparse dataset. We developed models to predict LC50-values, based on experimental LC50-data covering 2431 organic chemicals and 1506 aquatic species from the ECOTOX-database. Several well-known ML techniques were evaluated and a new ML model was developed, inspired by recommender systems. This new model involves a simple linear model that learns low-rank interactions between species and chemicals using factorization machines. We evaluated the predictive performances of the developed models based on two validation settings: 1) predicting unseen chemical-species pairs, and 2) predicting unseen chemicals. The results of this study show that ML models can accurately predict LC50-values in both validation settings. Moreover, we show that the novel factorization machine approach can match well-tuned, complex, ML approaches.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"765-788"},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10161234","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

Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm. 基于改进的马群优化算法的预测精油保留指数的定量结构-性质关系模型。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-10-01 Epub Date: 2023-11-03 DOI: 10.1080/1062936X.2023.2261855

A M Alharthi, D H Kadir, A M Al-Fakih, Z Y Algamal, N A Al-Thanoon, M K Qasim

{"title":"Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm.","authors":"A M Alharthi, D H Kadir, A M Al-Fakih, Z Y Algamal, N A Al-Thanoon, M K Qasim","doi":"10.1080/1062936X.2023.2261855","DOIUrl":"10.1080/1062936X.2023.2261855","url":null,"abstract":"The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"831-846"},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54230823","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

Prioritizing pharmaceutically active compounds (PhACs) based on occurrence-persistency-mobility-toxicity (OPMT) criteria: an application to the Brazilian scenario. 根据发生-持久性-流动性-毒性(OPMT)标准对药物活性化合物(PhACs)进行优先排序:在巴西的应用。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-10-01 Epub Date: 2023-12-04 DOI: 10.1080/1062936X.2023.2287516

V Roveri, L Lopes Guimarães, A T Correia

{"title":"Prioritizing pharmaceutically active compounds (PhACs) based on occurrence-persistency-mobility-toxicity (OPMT) criteria: an application to the Brazilian scenario.","authors":"V Roveri, L Lopes Guimarães, A T Correia","doi":"10.1080/1062936X.2023.2287516","DOIUrl":"10.1080/1062936X.2023.2287516","url":null,"abstract":"A study of Quantitative Structure Activity Relationship (QSAR) was performed to assess the possible adverse effects of 25 pharmaceuticals commonly found in the Brazilian water compartments and to establish a ranking of environmental concern. The occurrence (O), the persistence (P), the mobility (M), and the toxicity (T) of these compounds in the Brazilian drinking water reservoirs were evaluated. Moreover, to verify the predicted OPMT dataset outcomes, a quality index (QI) was also developed and applied. The main results showed that: (i) after in silico predictions through VEGA QSAR, 19 from 25 pharmaceuticals consumed in Brazil were classified as persistent; (ii) moreover, after in silico predictions through OPERA QSAR, 15 among those 19 compounds considered persistent, were also classified as mobile or very mobile. On the other hand, the results of toxicity indicate that only 9 pharmaceuticals were classified with the highest toxicity level. Ultimately, the QI of 7 from 25 pharmaceuticals were categorized as 'optimal'; 15 pharmaceuticals were categorized as 'good'; and only 3 pharmaceuticals were categorized as 'regular'. Therefore, based on the QI criteria used, it is possible to assume that this OPMT prediction dataset had a good reliability. Efforts to reduce emissions of OPMT-pharmaceuticals in Brazilian drinking water reservoirs are encouraged.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"34 12","pages":"1023-1039"},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478504","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

Correction. 校正

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-10-01 Epub Date: 2023-11-03 DOI: 10.1080/1062936X.2023.2266905

引用次数: 0

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project. 预测致突变性的QSAR模型的评估:第二届Ames/QSAR国际挑战项目的结果。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-10-01 Epub Date: 2023-12-04 DOI: 10.1080/1062936X.2023.2284902

A Furuhama, A Kitazawa, J Yao, C E Matos Dos Santos, J Rathman, C Yang, J V Ribeiro, K Cross, G Myatt, G Raitano, E Benfenati, N Jeliazkova, R Saiakhov, S Chakravarti, R S Foster, C Bossa, C Laura Battistelli, R Benigni, T Sawada, H Wasada, T Hashimoto, M Wu, R Barzilay, P R Daga, R D Clark, J Mestres, A Montero, E Gregori-Puigjané, P Petkov, H Ivanova, O Mekenyan, S Matthews, D Guan, J Spicer, R Lui, Y Uesawa, K Kurosaki, Y Matsuzaka, S Sasaki, M T D Cronin, S J Belfield, J W Firman, N Spînu, M Qiu, J M Keca, G Gini, T Li, W Tong, H Hong, Z Liu, Y Igarashi, H Yamada, K-I Sugiyama, M Honma

{"title":"Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.","authors":"A Furuhama, A Kitazawa, J Yao, C E Matos Dos Santos, J Rathman, C Yang, J V Ribeiro, K Cross, G Myatt, G Raitano, E Benfenati, N Jeliazkova, R Saiakhov, S Chakravarti, R S Foster, C Bossa, C Laura Battistelli, R Benigni, T Sawada, H Wasada, T Hashimoto, M Wu, R Barzilay, P R Daga, R D Clark, J Mestres, A Montero, E Gregori-Puigjané, P Petkov, H Ivanova, O Mekenyan, S Matthews, D Guan, J Spicer, R Lui, Y Uesawa, K Kurosaki, Y Matsuzaka, S Sasaki, M T D Cronin, S J Belfield, J W Firman, N Spînu, M Qiu, J M Keca, G Gini, T Li, W Tong, H Hong, Z Liu, Y Igarashi, H Yamada, K-I Sugiyama, M Honma","doi":"10.1080/1062936X.2023.2284902","DOIUrl":"10.1080/1062936X.2023.2284902","url":null,"abstract":"Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"34 12","pages":"983-1001"},"PeriodicalIF":3.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478503","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

QSPR models to predict the physical hazards of mixtures: a state of art. 预测混合物物理危害的QSPR模型：最新技术。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-07-01 DOI: 10.1080/1062936X.2023.2253150

G Fayet, P Rotureau

{"title":"QSPR models to predict the physical hazards of mixtures: a state of art.","authors":"G Fayet, P Rotureau","doi":"10.1080/1062936X.2023.2253150","DOIUrl":"10.1080/1062936X.2023.2253150","url":null,"abstract":"Physical hazards of chemical mixtures, associated for example with their fire or explosion risks, are generally characterized using experimental tools. These tests can be expensive, complex, long to perform and even dangerous for operators. Therefore, for several years and especially with the implementation of the REACH regulation, predictive methods like quantitative structure-property relationships have been encouraged as alternatives tests to determine (eco)toxicological but also physical hazards of chemical substances. Initially, these approaches were intended for pure products, by considering a molecular similarity principle. However, additional to those for pure products, QSPR models for mixtures recently appeared and represent an increasing field of research. This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"34 9","pages":"745-764"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10235530","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

3D-QSAR-based design, synthesis and biological evaluation of 2,4-disubstituted quinoline derivatives as antimalarial agents. 基于三维QSAR的抗疟药物2,4-二取代喹啉衍生物的设计、合成和生物学评价。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-07-01 DOI: 10.1080/1062936X.2023.2247326

V K Vyas, S Bhati, M Sharma, P Gehlot, N Patel, S Dalai

{"title":"3D-QSAR-based design, synthesis and biological evaluation of 2,4-disubstituted quinoline derivatives as antimalarial agents.","authors":"V K Vyas, S Bhati, M Sharma, P Gehlot, N Patel, S Dalai","doi":"10.1080/1062936X.2023.2247326","DOIUrl":"10.1080/1062936X.2023.2247326","url":null,"abstract":"2,4-Disubstituted quinoline derivatives were designed based on a 3D-QSAR study, synthesized and evaluated for antimalarial activity. A large dataset of 178 quinoline derivatives was used to perform a 3D-QSAR study using CoMFA and CoMSIA models. PLS analysis provided statistically validated results for CoMFA (r2ncv = 0.969, q2 = 0.677, r2cv = 0.682) and CoMSIA (r2ncv = 0.962, q2 = 0.741, r2cv = 0.683) models. Two series of a total of 40 2,4-disubstituted quinoline derivatives were designed with amide (quinoline-4-carboxamide) and secondary amine (4-aminoquinoline) linkers at the -C4 position of the quinoline ring. For the purpose of selecting better compounds for synthesis with good pEC50 values, activity prediction was carried out using CoMFA and CoMSIA models. Finally, a total of 10 2,4-disubstituted quinoline derivatives were synthesized, and screened for their antimalarial activity based on the reduction of parasitaemia. Compound #5 with amide linker and compound #19 with secondary amine linkers at the -C4 position of the quinoline ring showed maximum reductions of 64% and 57%, respectively, in the level of parasitaemia. In vivo screening assay confirmed and validated the findings of the 3D-QSAR study for the design of quinoline derivatives.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"34 8","pages":"639-659"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10501951","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

Pteridine reductase (PTR1): initial structure-activity relationships studies of potential leishmanicidal arylindole derivatives compounds. Pteridine还原酶（PTR1）：潜在杀利什曼原虫芳林多衍生物化合物的初步构效关系研究。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-07-01 Epub Date: 2023-08-22 DOI: 10.1080/1062936X.2023.2247331

J V Silva, S Sueyoshi, T J Snape, S Lal, J Giarolla

{"title":"Pteridine reductase (PTR1): initial structure-activity relationships studies of potential leishmanicidal arylindole derivatives compounds.","authors":"J V Silva, S Sueyoshi, T J Snape, S Lal, J Giarolla","doi":"10.1080/1062936X.2023.2247331","DOIUrl":"10.1080/1062936X.2023.2247331","url":null,"abstract":"Leishmaniasis is a public health concern, especially in Brazil and India. The drugs available for therapy are old, cause toxicity and have reports of resistance. Therefore, this paper aimed to carry out initial structure-activity relationships (applying molecular docking and dynamic simulations) of arylindole scaffolds against the pteridine reductase (PTR1), which is essential target for the survival of the parasite. Thus, we used a series of 43 arylindole derivatives as a privileged skeleton, which have been evaluated previously for different biological actions. Compound 7 stood out among its analogues presenting the best results of average number of interactions with binding site (2.00) and catalytic triad (1.00). Additionally, the same compound presented the best binding free energy (-32.33 kcal/mol) in dynamic simulations. Furthermore, with computational studies, it was possible to comprehend and discuss the influences of the substituent sizes, positions of substitutions in the aromatic ring and electronic influences. Therefore, this study can be a starting point for the structural improvements needed to obtain a good leishmanicidal drug.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"34 8","pages":"661-687"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10127226","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 cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach. 使用正则回归模型预测工程纳米颗粒的细胞毒性：一种计算机方法。

IF 3 3区环境科学与生态学

SAR and QSAR in Environmental Research Pub Date : 2023-07-01 DOI: 10.1080/1062936X.2023.2242785

A Valeriano, F Bondaug, I Ebardo, P Almonte, M A Sabugaa, J R Bagnol, M J Latayada, J M Macalalag, B D Paradero, M Mayes, M Balanay, A Alguno, R Capangpangan

{"title":"Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach.","authors":"A Valeriano, F Bondaug, I Ebardo, P Almonte, M A Sabugaa, J R Bagnol, M J Latayada, J M Macalalag, B D Paradero, M Mayes, M Balanay, A Alguno, R Capangpangan","doi":"10.1080/1062936X.2023.2242785","DOIUrl":"10.1080/1062936X.2023.2242785","url":null,"abstract":"The widespread application of engineered nanoparticles (NPs) in various industries has demonstrated their effectiveness over the years. However, modifications to NPs' physicochemical properties can lead to toxicological effects. Therefore, understanding the toxicity behaviour of NPs is crucial. In this paper, regularized regression models, such as ridge, LASSO, and elastic net, were constructed to predict the cytotoxicity of various engineered NPs. The dataset utilized in this study was compiled from several journals published between 2010 and 2022. Data exploration revealed missing values, which were addressed through listwise deletion and kNN imputation, resulting in two complete datasets. The ridge, LASSO, and elastic net models achieved F1 scores ranging from 91.81% to 92.65% during internal validation and 92.89% to 93.63% during external validation on Dataset 1. On Dataset 2, the models attained F1 scores between 92.16% and 92.43% during internal validation and 92% and 92.6% during external validation. These results indicate that the developed models effectively generalize to unseen data and demonstrate high accuracy in classifying cytotoxicity levels. Furthermore, the cell type, material, cell source, cell tissue, synthesis method, and coat or functional group were identified as the most important descriptors by the three models across both datasets.","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":"34 7","pages":"591-604"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10325987","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