Prediction of Milk/Plasma Drug Concentration (M/P) Ratio Using Support Vector Machine (SVM) Method

Zhao, Chunyan; Zhang, Haixia; Zhang, Xiaoyun; Zhang, Ruisheng; Luan, Feng; Liu, Mancang; Hu, Zhide; Fan, Botao

doi:10.1007/s11095-005-8716-4

Cited by 28 publications

(33 citation statements)

References 29 publications

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“…11,13,39,40 SVC was originally developed by Vapnik and coworkers 11 and it has shown promising capability for solving a number of biological classification problems. 14,[16][17][18][19][20][23][24][25][26][27][28][29] SVC is based on the structure risk minimization (SRM) principle from statistical learning theory. 11 For linearly separable cases, SVC performs classification tasks by constructing a hyperplane in a multidimensional space to separate two different classes of feature vectors with a maximum margin.…”

Section: Svc Algorithmmentioning

confidence: 99%

“…On the other hand, it has been reported that some ML methods such as support vector classification (SVC) have consistently shown excellent performance for the prediction of Pglycoprotein substrates, 16 tetrahymena pyriformis toxicity chemicals, 20 ADME property, 23 milk/plasma drug concentration (M/P) ratio, 24 estrogen receptor-beta ligands, 25 cytochrome inhibitors, 26 factor Xa inhibitors, 27 anti-HIV nucleoside derivatives, 28 and human pregnane X receptor activation. 29 Therefore, it is desirable to test the usefulness of SVC and other ML methods as potential tools for the prediction of antibacterial compounds.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of antibacterial compounds by machine learning approaches

et al. 2008

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The machine learning (ML) as well as quantitative structure activity relationship (QSAR) method has been explored for predicting compounds with antibacterial activities at impressive performance. It is desirable to test additional ML methods, select most representative sets of molecular descriptors, and subject the developed prediction models to rigorous evaluations. This work evaluated three ML methods, support vector classification (SVC), k-nearest neighbor (k-NN), and C4.5 decision tree, which were trained and tested by 230 antibacterial and 381 nonantibacterial compounds. A well-established feature selection method was used to select representative molecular descriptors from a larger pool than that used in reported studies. The performance of the developed prediction models was tested by 5-fold cross-validation and independent evaluation set. SVC produced the best prediction accuracies of 96.66 and 98.15% for antibacterial compounds, and 99.50 and 98.02% for nonantibacterial compounds respectively, which are slightly improved against those of the reported ML as well as QSAR models and outperform the k-NN and C4.5 decision tree models developed in this work. Our study suggests that ML methods, particularly SVC, are potentially useful for facilitating the discovery of antibacterial agents.

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Section: Svc Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of antibacterial compounds by machine learning approaches

et al. 2008

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“…Katritzky et al [7] investigated the prediction of M/P ratios for a set of 115 drugs using multiple linear regression. Zhao et al [20] used a support vector machine, SVM, method to analyze M/P ratios for 126 drugs. The only statistic they gave was an 'accuracy' of 90.48 %.…”

Section: Ei ð%þ ¼ 100 â ðM=pþ â A=infantdrugclearance ð1þmentioning

confidence: 99%

“…[20][21][22] The advantage of including such a wide range of molecules for development of quantitative models is that the models encompass a wide range of applicability domain and hence can be successfully utilized for prediction of a variety of untested molecules. The data were assigned as high risk (H) (M/P > 1) and low risk (L) (M/P < 1) drugs as proposed by Malone et al [25] for classification based QSTR model.…”

Section: Datasetmentioning

confidence: 99%

Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

Kar

Roy

2013

Molecular Informatics

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A large set of 185 compounds with diverse molecular structures and different mechanisms of therapeutic actions was used to develop and validate statistically significant classification and regression based QSTR models for predicting partitioning of drugs/chemicals into breast milk. Pharmacophore mapping was also carried out which showed four important features required for lower risk of secretion into milk: (i) hydrophobic group (HYD), (ii) ring aromatic group (RA), (iii) negative ionizable (NegIon) and (iv) hydrogen bond donor (HBA). QSTR and pharmacophore models were rigorously validated internally as well as externally to check the possibilities of any chance correlation and judge the predictive potential of the models. Pharmacological distribution diagrams (PDDs) were used for the classification model as a visualizing technique for the identification and selection of chemicals with lower partitioning into milk. Our in silico models enable to identify the essential structural attributes and quantify the prime molecular pre-requisites which were chiefly responsible for secretion into milk. The developed models were also implemented to screen milk/plasma partitioning potential for a huge number DrugBank database (http://www.drugbank.ca/) compounds.

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“…Then, research on its theoretical and application aspects thrived. It has been applied in a wide range of problems, including text categorization (de Vel et al, 2001;Kim et al, 2001), image classification and object detection (Ben-Yacoub et al, 1999;Karlsen et al, 2000), flood stage forecasting (Liong and Sivapragasam, 2002), microarray gene expression data analysis (Brown et al, 2000), drug design (Zhao et al, 2006a(Zhao et al, , 2006b, protein solvent accessibility prediction (Yuan et al, 2002), and protein fold prediction (Ding and Dubchak, 2001;Hua and Sun, 2001). Many studies have demonstrated that SVM was consistently superior to other supervised learning methods (Brown et al, 2000;Burbidge et al, 2001;Cai et al, 2003).…”

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confidence: 99%

Computational Identification of Potential Molecular Interactions in Arabidopsis

Lin

Chen

et al. 2009

Plant Physiology

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Knowledge of the protein interaction network is useful to assist molecular mechanism studies. Several major repositories have been established to collect and organize reported protein interactions. Many interactions have been reported in several model organisms, yet a very limited number of plant interactions can thus far be found in these major databases. Computational identification of potential plant interactions, therefore, is desired to facilitate relevant research. In this work, we constructed a support vector machine model to predict potential Arabidopsis (Arabidopsis thaliana) protein interactions based on a variety of indirect evidence. In a 100-iteration bootstrap evaluation, the confidence of our predicted interactions was estimated to be 48.67%, and these interactions were expected to cover 29.02% of the entire interactome. The sensitivity of our model was validated with an independent evaluation data set consisting of newly reported interactions that did not overlap with the examples used in model training and testing. Results showed that our model successfully recognized 28.91% of the new interactions, similar to its expected sensitivity (29.02%). Applying this model to all possible Arabidopsis protein pairs resulted in 224,206 potential interactions, which is the largest and most accurate set of predicted Arabidopsis interactions at present. In order to facilitate the use of our results, we present the Predicted Arabidopsis Interactome Resource, with detailed annotations and more specific per interaction confidence measurements. This database and related documents are freely accessible at

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Prediction of Milk/Plasma Drug Concentration (M/P) Ratio Using Support Vector Machine (SVM) Method

Cited by 28 publications

References 29 publications

Prediction of antibacterial compounds by machine learning approaches

Prediction of antibacterial compounds by machine learning approaches

Prediction of Milk/Plasma Concentration Ratios of Drugs and Environmental Pollutants Using In Silico Tools: Classification and Regression Based QSARs and Pharmacophore Mapping

Computational Identification of Potential Molecular Interactions in Arabidopsis

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