QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes

Yang, Xiaoyan; Li, Minjie; Su, Qiang; Wu, Milin; Gu, Tianyu; Lu, Wei

doi:10.1007/s00044-013-0527-2

Cited by 16 publications

(7 citation statements)

References 46 publications

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“…Then they can get the predicted result after waiting for some time. It is a remarkable advance compared to our previous work [17, 20, 36]. …”

Section: Resultsmentioning

confidence: 72%

“…Moreover, several efforts by using computational and mathematical approaches have been made in investigating small molecules of DPP-4 inhibitors. In our previous studies [17], we have attempted to use the quantum chemistry method [18] to optimize a series of DPP-IV inhibitors, and a 2D-QSAR model has been built, which can predict the inhibitory activity of small molecule with satisfying results. However, it is time consuming to calculate the molecular descriptors adopted in 2D-QSAR model.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

Yang

et al. 2013

BioMed Research International

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The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction.

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“…Then they can get the predicted result after waiting for some time. It is a remarkable advance compared to our previous work [17, 20, 36]. …”

Section: Resultsmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

Yang

et al. 2013

BioMed Research International

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“…Due to the existence of irrelevant or redundant features redundancy of the parameters, it is necessary to select the parameter most relevant to the target variable, especially when the sample set is small. The purpose of feature selection is to select a variables subset of n features from the set of m obtained variables ( n < m ) without significantly reducing the predictive ability of the model 27 . In this work, the total number of calculated molecular descriptors was eight.…”

Section: Resultsmentioning

confidence: 99%

“…In addition to the above algorithm, support vector regression (SVR) is a useful machine learning algorithms that can be used to solve linear and nonlinear problems 25 , especially for small sample sizes. It has been proved to be suitable for the QSAR analyses of flavonoids 26 , drug activity prediction and design 27 . For instance, Minaoui et al have used support vector regression to investigate the relationship between structure and activity of 38 cyclicurea derivatives, inhibiting HIV protease.…”

Section: Introductionmentioning

confidence: 99%

Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

Shi

2021

Sci Rep

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The Support vector regression (SVR) was used to investigate quantitative structure–activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.

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“…34,35 In order to evaluate the feature selection, the root-mean-square error (RMSE) was employed as the measure of goodness of fit. The RMSE 36 is defined as follows:…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area

Shi

Chang

et al. 2018

J. Chem. Inf. Model.

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The specific surface area (SSA) of ABO3-type perovskite is one of the important properties associated with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (in the range of 1–60 m2 g–1) of perovskite and its features, including chemical compositions and technical parameters. The genetic algorithm–support vector regression method was used to screen the main features for modeling. The correlation coefficient (R) between the predicted and experimental SSAs reached as high as 0.986 for the training data set and 0.935 for leave-one-out cross-validation. ABO3-type perovskites with higher SSA can be screened out using the Online Computation Platform for Materials Data Mining (OCPMDM) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite, which is accessible at .

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QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes

Cited by 16 publications

References 46 publications

Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area

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