EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES
Structure-Toxicity Relationships have been studied for a set of 42 organophosphorous pesticides (OPs) through multiple linear regression (MLR) and artificial neural networks (ANN). A model with three descriptors, including: total lipophilicity [log (P)], widths radicals R1 [(LR1)] and R2 [(LR2)] ha...
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| Format: | Article |
| Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2019-03-01
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| Series: | Journal of Engineering Studies and Research |
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| Online Access: | https://jesr.ub.ro/index.php/1/article/view/39 |
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| _version_ | 1823863580972285952 |
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| author | HANANE FIKRI TAOUFIQ FECHTALI MOHAMED MAMOUMI |
| author_facet | HANANE FIKRI TAOUFIQ FECHTALI MOHAMED MAMOUMI |
| author_sort | HANANE FIKRI |
| collection | DOAJ |
| description |
Structure-Toxicity Relationships have been studied for a set of 42 organophosphorous pesticides (OPs) through multiple linear regression (MLR) and artificial neural networks (ANN). A model with three descriptors, including: total lipophilicity [log (P)], widths radicals R1 [(LR1)] and R2 [(LR2)] has achieved good results in phase Training and phase prediction of toxicity [log LD50 (lethal dose 50, Oral rat)]. The linear model (MLR: n=40, r²=0.86, s=40 and q2 = 0.66) and non-linear model with a configuration [3-6-1] (ANN: r²=0.95, s=0.73 and q2 = 0.17) have proved very successful and complementary. The selected descriptors indicate the importance of lipophilicity and widths radicals R1 and R2 in the contribution of the toxicity of pesticides derived from OPs used in this study. This information is relevant for the design of a new model of non-toxic pesticides OPs.
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| format | Article |
| id | doaj-art-1afe0d031d79430a96974fce4234cce7 |
| institution | Kabale University |
| issn | 2068-7559 2344-4932 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
| record_format | Article |
| series | Journal of Engineering Studies and Research |
| spelling | doaj-art-1afe0d031d79430a96974fce4234cce72025-02-09T11:37:49ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322019-03-01251EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDESHANANE FIKRI0TAOUFIQ FECHTALI1MOHAMED MAMOUMI2Laboratory of Neurosciences, Integrated Physiopathology and Natural Substances - F.S.T. Mohammedia; BP:146 Mohammedia 20650, MorrocoLaboratory of Neurosciences, Integrated Physiopathology and Natural Substances - F.S.T. Mohammedia; BP:146 Mohammedia 20650, MorrocoLaboratory of Neurosciences, Integrated Physiopathology and Natural Substances - F.S.T. Mohammedia; BP:146 Mohammedia 20650, Morroco Structure-Toxicity Relationships have been studied for a set of 42 organophosphorous pesticides (OPs) through multiple linear regression (MLR) and artificial neural networks (ANN). A model with three descriptors, including: total lipophilicity [log (P)], widths radicals R1 [(LR1)] and R2 [(LR2)] has achieved good results in phase Training and phase prediction of toxicity [log LD50 (lethal dose 50, Oral rat)]. The linear model (MLR: n=40, r²=0.86, s=40 and q2 = 0.66) and non-linear model with a configuration [3-6-1] (ANN: r²=0.95, s=0.73 and q2 = 0.17) have proved very successful and complementary. The selected descriptors indicate the importance of lipophilicity and widths radicals R1 and R2 in the contribution of the toxicity of pesticides derived from OPs used in this study. This information is relevant for the design of a new model of non-toxic pesticides OPs. https://jesr.ub.ro/index.php/1/article/view/39multiple linear regression (MLR)artificial neural networks (ANN)organophosphorous pesticides (OPS)LD50descriptors |
| spellingShingle | HANANE FIKRI TAOUFIQ FECHTALI MOHAMED MAMOUMI EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES Journal of Engineering Studies and Research multiple linear regression (MLR) artificial neural networks (ANN) organophosphorous pesticides (OPS) LD50 descriptors |
| title | EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES |
| title_full | EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES |
| title_fullStr | EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES |
| title_full_unstemmed | EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES |
| title_short | EVALUATION OF THE MODEL PREDICTION TOXICITY (LD50) FOR SERIES OF 42 ORGANOPHOSPHORUS PESTICIDES |
| title_sort | evaluation of the model prediction toxicity ld50 for series of 42 organophosphorus pesticides |
| topic | multiple linear regression (MLR) artificial neural networks (ANN) organophosphorous pesticides (OPS) LD50 descriptors |
| url | https://jesr.ub.ro/index.php/1/article/view/39 |
| work_keys_str_mv | AT hananefikri evaluationofthemodelpredictiontoxicityld50forseriesof42organophosphoruspesticides AT taoufiqfechtali evaluationofthemodelpredictiontoxicityld50forseriesof42organophosphoruspesticides AT mohamedmamoumi evaluationofthemodelpredictiontoxicityld50forseriesof42organophosphoruspesticides |