Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling
Abstract Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed...
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Nature Portfolio
2024-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-63708-2 |
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| author | Mohsen Abbod Ahmad Mohammad |
| author_facet | Mohsen Abbod Ahmad Mohammad |
| author_sort | Mohsen Abbod |
| collection | DOAJ |
| description | Abstract Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed using CA and IA models. QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. The ANN model showed slightly better predictability than the SVM model, with R2 and R2 cv at 0.91 and 0.81, respectively. For external validation, the R2 test value was 0.845. In contrast, the SVM model had values of 0.91, 0.78, and 0.77 for the same metrics. In conclusion, the proposed ML-based model can be a valuable tool for developing potent fungicidal mixtures to delay fungicidal resistance emergence. |
| format | Article |
| id | doaj-art-11b5a896ae524f74bb08c354a2de743b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-11b5a896ae524f74bb08c354a2de743b2025-01-12T12:25:01ZengNature PortfolioScientific Reports2045-23222024-06-0114111210.1038/s41598-024-63708-2Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modelingMohsen Abbod0Ahmad Mohammad1Department of Plant Protection, Faculty of Agriculture, Al-Baath UniversityDepartment of Plant Protection, Faculty of Agriculture, Al-Baath UniversityAbstract Fungicide mixtures are an effective strategy in delaying the development of fungicide resistance. In this research, a fixed ratio ray design method was used to generate fifty binary mixtures of five fungicides with diverse modes of action. The interaction of these mixtures was then analyzed using CA and IA models. QSAR modeling was conducted to assess their fungicidal activity through multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN). Most mixtures exhibited additive interaction, with the CA model proving more accurate than the IA model in predicting fungicidal activity. The MLR model showed a good linear correlation between selected theoretical descriptors by the genetic algorithm and fungicidal activity. However, both ML-based models demonstrated better predictive performance than the MLR model. The ANN model showed slightly better predictability than the SVM model, with R2 and R2 cv at 0.91 and 0.81, respectively. For external validation, the R2 test value was 0.845. In contrast, the SVM model had values of 0.91, 0.78, and 0.77 for the same metrics. In conclusion, the proposed ML-based model can be a valuable tool for developing potent fungicidal mixtures to delay fungicidal resistance emergence.https://doi.org/10.1038/s41598-024-63708-2SVMANNSynergismAntagonistPesticide mixtures |
| spellingShingle | Mohsen Abbod Ahmad Mohammad Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling Scientific Reports SVM ANN Synergism Antagonist Pesticide mixtures |
| title | Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling |
| title_full | Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling |
| title_fullStr | Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling |
| title_full_unstemmed | Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling |
| title_short | Combined interaction of fungicides binary mixtures: experimental study and machine learning-driven QSAR modeling |
| title_sort | combined interaction of fungicides binary mixtures experimental study and machine learning driven qsar modeling |
| topic | SVM ANN Synergism Antagonist Pesticide mixtures |
| url | https://doi.org/10.1038/s41598-024-63708-2 |
| work_keys_str_mv | AT mohsenabbod combinedinteractionoffungicidesbinarymixturesexperimentalstudyandmachinelearningdrivenqsarmodeling AT ahmadmohammad combinedinteractionoffungicidesbinarymixturesexperimentalstudyandmachinelearningdrivenqsarmodeling |