English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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%.

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