Prediction of aquatic toxicity of energetic materials using genetic function approximation
ABSTRACT: The first attempt to use genetic function approximation (GFA) for prediction of aquatic toxicity of soluble energetic materials is reported in this paper. The prediction is based on the estimation of the luminescent bacteria Aliivibrio fischeri inhibition in water according to the recently...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2023-03-01
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| Series: | FirePhysChem |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667134422000323 |
| _version_ | 1811167249360224256 |
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| author | Sergey V. Bondarchuk |
| author_facet | Sergey V. Bondarchuk |
| author_sort | Sergey V. Bondarchuk |
| collection | DOAJ |
| description | ABSTRACT: The first attempt to use genetic function approximation (GFA) for prediction of aquatic toxicity of soluble energetic materials is reported in this paper. The prediction is based on the estimation of the luminescent bacteria Aliivibrio fischeri inhibition in water according to the recently reported experimental results. Thus, two quantitative structure-activity relationship (QSAR) models for 15 min and 30 min exposure were obtained, which include five and six essential descriptors, respectively. Most of them are so-called “fast descriptors” assuming there is no need for quantum-chemical calculations. The rest descriptors are obtained in terms of semi-empirical approach allowing the prediction to be rapidly complete. The developed QSAR models provide relatively high correlation coefficients, namely, R2 = 0.81 and 0.82 for 15 min and 30 min datasets, respectively. The experimental datasets included a number of values, which were presented ambiguously (< or > than certain values). Thus, these have not been included (13 for 15 min and 10 for 30 min datasets) in the training sets and used them as the corresponding test sets. As a result, the developed models accurately indicate what exactly the higher and lower values should be applied instead of ones presented with ambiguity. Thus, the results may be useful for predicting the aquatic toxicity of new nitrogen-rich energetic materials, both molecular and ionic, bearing nitro, nitramino, azido groups and other commonly used explosophores. |
| first_indexed | 2024-04-10T16:06:07Z |
| format | Article |
| id | doaj.art-8d2f1f28695f4182be1a22c796946918 |
| institution | Directory Open Access Journal |
| issn | 2667-1344 |
| language | English |
| last_indexed | 2024-04-10T16:06:07Z |
| publishDate | 2023-03-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | FirePhysChem |
| spelling | doaj.art-8d2f1f28695f4182be1a22c7969469182023-02-10T04:23:38ZengKeAi Communications Co. Ltd.FirePhysChem2667-13442023-03-01312328Prediction of aquatic toxicity of energetic materials using genetic function approximationSergey V. Bondarchuk0Corresponding author.; Department of Chemistry and Nanomaterials Science, Bogdan Khmelnitsky Cherkasy National University, blvd. Shevchenko 81, 18031 Cherkasy, UkraineABSTRACT: The first attempt to use genetic function approximation (GFA) for prediction of aquatic toxicity of soluble energetic materials is reported in this paper. The prediction is based on the estimation of the luminescent bacteria Aliivibrio fischeri inhibition in water according to the recently reported experimental results. Thus, two quantitative structure-activity relationship (QSAR) models for 15 min and 30 min exposure were obtained, which include five and six essential descriptors, respectively. Most of them are so-called “fast descriptors” assuming there is no need for quantum-chemical calculations. The rest descriptors are obtained in terms of semi-empirical approach allowing the prediction to be rapidly complete. The developed QSAR models provide relatively high correlation coefficients, namely, R2 = 0.81 and 0.82 for 15 min and 30 min datasets, respectively. The experimental datasets included a number of values, which were presented ambiguously (< or > than certain values). Thus, these have not been included (13 for 15 min and 10 for 30 min datasets) in the training sets and used them as the corresponding test sets. As a result, the developed models accurately indicate what exactly the higher and lower values should be applied instead of ones presented with ambiguity. Thus, the results may be useful for predicting the aquatic toxicity of new nitrogen-rich energetic materials, both molecular and ionic, bearing nitro, nitramino, azido groups and other commonly used explosophores.http://www.sciencedirect.com/science/article/pii/S2667134422000323ToxicityNitrogen-rich compoundEnergetic materialQSAR |
| spellingShingle | Sergey V. Bondarchuk Prediction of aquatic toxicity of energetic materials using genetic function approximation FirePhysChem Toxicity Nitrogen-rich compound Energetic material QSAR |
| title | Prediction of aquatic toxicity of energetic materials using genetic function approximation |
| title_full | Prediction of aquatic toxicity of energetic materials using genetic function approximation |
| title_fullStr | Prediction of aquatic toxicity of energetic materials using genetic function approximation |
| title_full_unstemmed | Prediction of aquatic toxicity of energetic materials using genetic function approximation |
| title_short | Prediction of aquatic toxicity of energetic materials using genetic function approximation |
| title_sort | prediction of aquatic toxicity of energetic materials using genetic function approximation |
| topic | Toxicity Nitrogen-rich compound Energetic material QSAR |
| url | http://www.sciencedirect.com/science/article/pii/S2667134422000323 |
| work_keys_str_mv | AT sergeyvbondarchuk predictionofaquatictoxicityofenergeticmaterialsusinggeneticfunctionapproximation |