Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds

Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun‐Bo; Zhang, Kejun; Yao, Xiaojun

doi:10.1016/j.ejmech.2009.04.039

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Similar performances of GEP were obtained in comparison with SVM ( r = 0.91 versus r = 0.86 on the training set, including 36 compounds). GEP technique has also been profitably used for the prediction of IC 50 values of 38 imidazopyridine anticoccidial compounds ( r = 0.96 and r = 0.91 on the training set and the test set, respectively) …”

Section: Applications In Pharmacodynamicsmentioning

confidence: 99%

“…GEP technique has also been profitably used for the prediction of IC 50 values of 38 imidazopyridine anticoccidial compounds (r ) 0.96 and r ) 0.91 on the training set and the test set, respectively). 78 Both regression and classification analysis can be applied to the same problem to obtain different information on binding affinity and selectivity of the compounds. This approach has been utilized to predict the specificity and subtype selectivity of 5HT1E and 5HT1F serotonin receptors ligands.…”

Section: Qsarmentioning

confidence: 99%

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Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Michielan

Moro

2010

J. Chem. Inf. Model.

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Section: Applications In Pharmacodynamicsmentioning

confidence: 99%

Section: Qsarmentioning

confidence: 99%

Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Michielan

Moro

2010

J. Chem. Inf. Model.

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“…It is important to avoid the collinearity in the process of developing of the multiple regression equation [19]. The correlation coefficient between descriptors is given in the Fig.3 with the good result of 0.065, which implies that the statistical relationship between descriptors is extremely small and proves the statistical reliability of the method.…”

Section: Discussionmentioning

confidence: 99%

QSAR Model Based Gradient Boosting Regression of N-Arylsulfonyl-Indole-2-Carboxamide Derivatives as Inhibitors for Fructose-1,6-Bisphosphatase

Zhao

Yang

et al. 2021

Preprint

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As is known to all, diabetes metellius is a global health threaten and it has caused worldwide attention of scientists. To get a better investigation of the drug design of diabetes, we used heuristic method to established the linear model and used Gradient Boosting Regression to establish the nonlinear model of Fructose-1,6-Bisphosphatse inhibitor successively. In this study, 84 derivatives of N-Arylsulfonyl-Indole-2-Carboxamide were introduced into the models, two outstanding QSAR models with 2 molecule descriptors were established successfully. Grandient Boosting Regression rendered a good correlation with R2 of 0.943 and MSE of 0.135 for the training set, 0.916 and 0.213 for test set, which also proves the feasibility of the implementation of the new method GBR in the field of QSAR. Meanwhile, the optimal model displayed wonderful statistical significance. This study shows unlimited potential for design of new drugs for diabetes.

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“…Regarding the possible solutions in the problem domain as an individual or a chromosome of the group, coding the individual into the form of symbol string, carrying out repeated operation on the group based on the genetics (genetics, intersection, and heteromorphosis), evaluating the individuals according to the scheduled target fitness function, constantly obtaining better groups according to the evolution rule of “survival of the fittest,” and, meanwhile, searching the optimum individual with the searching approach in the overall situation to obtain the satisfied and optimal solutions, it has extremely strong generalization ability and has been used for the QSAR study of the drug [22–25]. …”

Section: Introductionmentioning

confidence: 99%

Prediction on the Inhibition Ratio of Pyrrolidine Derivatives on Matrix Metalloproteinase Based on Gene Expression Programming

You

Jia

et al. 2014

BioMed Research International

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Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R 2) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

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Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds

Cited by 8 publications

References 19 publications

Pharmaceutical Perspectives of Nonlinear QSAR Strategies

Pharmaceutical Perspectives of Nonlinear QSAR Strategies

QSAR Model Based Gradient Boosting Regression of N-Arylsulfonyl-Indole-2-Carboxamide Derivatives as Inhibitors for Fructose-1,6-Bisphosphatase

Prediction on the Inhibition Ratio of Pyrrolidine Derivatives on Matrix Metalloproteinase Based on Gene Expression Programming

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