Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach
Nowadays, quantitative structure–activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds t...
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MDPI AG
2018-04-01
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| Series: | Molecules |
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| Online Access: | http://www.mdpi.com/1420-3049/23/5/1002 |
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| author | Feng Luan Ting Wang Lili Tang Shuang Zhang M. Natália Dias Soeiro Cordeiro |
| author_facet | Feng Luan Ting Wang Lili Tang Shuang Zhang M. Natália Dias Soeiro Cordeiro |
| author_sort | Feng Luan |
| collection | DOAJ |
| description | Nowadays, quantitative structure–activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO2, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R2 = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R2 = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R2 = 0.803, RMS = 0.222, RBFNN: n = 60, R2 = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R2 = 0.852, RMS = 0.192; RBFNN: n = 31, R2 = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity. |
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| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-17T13:49:49Z |
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| spelling | doaj.art-8b94ccf003294d68b1cf6ee5b46ca4272022-12-21T21:46:04ZengMDPI AGMolecules1420-30492018-04-01235100210.3390/molecules23051002molecules23051002Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR ApproachFeng Luan0Ting Wang1Lili Tang2Shuang Zhang3M. Natália Dias Soeiro Cordeiro4College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, ChinaCollege of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, ChinaCollege of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, ChinaCollege of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, ChinaLAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, PortugalNowadays, quantitative structure–activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO2, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R2 = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R2 = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R2 = 0.803, RMS = 0.222, RBFNN: n = 60, R2 = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R2 = 0.852, RMS = 0.192; RBFNN: n = 31, R2 = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.http://www.mdpi.com/1420-3049/23/5/1002substituted aromatic compoundstoxicityquantitative structure–activity relationship (QSAR)multiple linear regression (MLR)radial basis function neural network (RBFNN) |
| spellingShingle | Feng Luan Ting Wang Lili Tang Shuang Zhang M. Natália Dias Soeiro Cordeiro Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach Molecules substituted aromatic compounds toxicity quantitative structure–activity relationship (QSAR) multiple linear regression (MLR) radial basis function neural network (RBFNN) |
| title | Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach |
| title_full | Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach |
| title_fullStr | Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach |
| title_full_unstemmed | Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach |
| title_short | Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach |
| title_sort | estimation of the toxicity of different substituted aromatic compounds to the aquatic ciliate tetrahymena pyriformis by qsar approach |
| topic | substituted aromatic compounds toxicity quantitative structure–activity relationship (QSAR) multiple linear regression (MLR) radial basis function neural network (RBFNN) |
| url | http://www.mdpi.com/1420-3049/23/5/1002 |
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