Topological Models for Prediction of Pharmacokinetic Parameters of Cephalosporins using Random Forest, Decision Tree and Moving Average Analysis

Abstract

The topological indices were used to encode the structureal features of cephalosporins. Both topostructural and topochemical versions of a distance based descriptor, three adjacency based descriptors and five distance-cum-adjacency based descriptors were calculated. The values of 18 indices for each cephalosporin in the dataset were computed using an in-house computer program. Multiple pharmacokinetic parameters of cephalosporins were predicted using random forest, decision tree and moving average analysis. Random forest correctly classified the pharmacokinetic parameters into low and high ranges upto 95%. A decision tree was constructed for each pharmacokinetic parameter to determine the importance of topological indices. The decision tree learned the information from the input data with an accuracy of 95% and correctly predicted the cross-validated (10 fold) data with an accuracy of upto 90%. Three independent moving average based topological models were developed using a single range for simultaneous prediction of multiple pharmacokinetic parameters. The accuracy of classification of single index based models using moving average analysis varied from 65% to 100%.378

Diverse classification models for anti-hepatitis C virus activity of thiourea derivatives

“…In this manner, DT created an interactive branching topology in which the branch taken at each intersection is determined by a rule related to a descriptor of the molecule and lastly, each terminating leaf of the tree is assigned to a particular category i.e. A (active) or B (inactive) [22]. In this study, a dataset of 121 thiourea derivatives was divided into training and test set.…”

Improved superaugmented eccentric connectivity indices

“…The values of SAc n 4 c , SAc n 5 c , SAc n 6 c , and SAc n 7 c were computed for each analog of data set using an in-house computer program. Resulting data was analyzed and suitable topochemical models were developed after identification of the active ranges by maximization of moving average with regard to active derivatives (\35% = inactive, 35-65% = transitional, [65% = active) (Gupta et al, 2001;Dureja et al, 2008b). Subsequently, two important biological activities were assigned to each analog, which were then compared with the reported hCE1 and hiCE inhibitory activities of isatins (Hyatt et al, 2007a).…”

Models for anti-inflammatory activity of 8-substituted-4-anilino-6-aminoquinoline-3-carbonitriles

“…Index values of all the 44 chosen descriptors were analyzed and suitable models were developed after identification of the active ranges by maximization of moving average with respect to active compounds (\35% = inactive, 35-65% = transitional, [65% = active) (Gupta et al, 2001;Dureja et al, 2008b). Subsequently, each analogue of data set was assigned a biological activity using these models, which was then compared with the reported activity .…”