2015
DOI: 10.1039/c5ra05663b
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Support vector machine (SVM) classification model based rational design of novel tetronic acid derivatives as potent insecticidal and acaricidal agents

Abstract: A novel support vector machine (SVM) classification model was established for distinguishing potent and weak/inactive insecticides. Classification model-based rational design of novel tetronic acid derivatives was then performed to choose the preferable site of spirotetramat for chemical modification.Afterwards, eleven C5 0 -oxime ether-derived spirotetramat analogues, which are indicated as "potent class", were synthesized and validated by biological assays, revealing that theoretical estimates are significan… Show more

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Cited by 13 publications
(10 citation statements)
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“…SVM is a widely used classier that uses supervised machine learning methods. 31,32 The purpose of the SVM is to construct an optimal hyperplane that separates the sample into its maximum margins. The SVM handles the classication of nonlinear data by nonlinear mapping the input space to the higher dimensional feature space using the appropriate kernel.…”
Section: Biometric Data and Oct-ct Fusion Imagementioning
confidence: 99%
“…SVM is a widely used classier that uses supervised machine learning methods. 31,32 The purpose of the SVM is to construct an optimal hyperplane that separates the sample into its maximum margins. The SVM handles the classication of nonlinear data by nonlinear mapping the input space to the higher dimensional feature space using the appropriate kernel.…”
Section: Biometric Data and Oct-ct Fusion Imagementioning
confidence: 99%
“…14 There is now growing interest in applying mathematical methods to improve the accuracy of molecular docking, including multiple linear regression (MLR), partial least squares (PLS) and support vector machines (SVMs). 15 In our previous study, 16 we have demonstrated that these regression methods are indeed feasible to establish a reliable structure-based QSAR model by employing a ligand fit scoring function and the structural characteristics of the compounds. In doing so, we have been able to significantly improve the performance of QSAR analysis.…”
Section: Introductionmentioning
confidence: 99%
“…In doing so, we have been able to significantly improve the performance of QSAR analysis. 17 As part of our continued interest in rational drug design, 16,[18][19][20][21][22] we herein describe the development and validation of linear as well as non-linear regression-and docking-based models for the computational identification of a series of B-Raf V600E inhibitors. Three regression methods were applied to establish reliable, structure-based QSAR models.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, we envisioned that an integration of ligand-based pharmacophore model and structure-based molecular docking can be conducted with respect to JAK2 inhibitors, and the development of JAK2 inhibitors with new scaffolds can be assisted and accelerated by such strategy. In connection with our previous work on computational medicinal chemistry, 17 herein, we reported the establishment of pharmacophore and molecular docking models of structurally diverse JAK2 inhibitors, and further sequential virtual screening on commercial SPECS database. To our delight, multiple-substituted aminothiazole derivative B2 bearing the novel scaffolds, the skeleton of which is remarkably different from the existing JAK2 inhibitors.…”
Section: -10mentioning
confidence: 99%