Computation of models for prediction of blood brain barrier permeability using molecular descriptors

Authors:
Vikas Sharma;Harish Dureja;Vipin Kumar
Affiliations:
Institute of Pharmaceutical Sciences, Kurukshetra University, Kurukshetra, Haryana, India;Faculty of Pharmaceutical Sciences, M.D. University, Rohtak, Haryana, India;Institute of Pharmaceutical Sciences, Kurukshetra University, Kurukshetra, Haryana, India
Venue:
Journal of Computational Methods in Sciences and Engineering
Year:
2013

Citing 6
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Artificial intelligence: a modern approach

Artificial intelligence: a modern approach
Random Forests

Machine Learning
An empirical evaluation of supervised learning in high dimensions

Proceedings of the 25th international conference on Machine learning
A prediction model for blood-brain barrier permeation and analysis on its parameter biologically

Computer Methods and Programs in Biomedicine
Models for prediction of anti- HIV-1 activity of 5-Alkyl-2-alkylamino-6-(2,6-difluorophenylalkyl)-3,4-dihydropyrimidin-4(3H)-ones using random forest, decision tree and moving average analysis

Journal of Computational Methods in Sciences and Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Models for prediction of Blood Brain Barrier permeability were developed in the present study. A dataset of 63 structurally diverse compounds was selected for the present investigation. The values of 21 Constitutional descriptors, Topological descriptor and Information indices for each compound of the data set were computed using freely available e-Dragon software. Data generated was analyzed and suitable models were developed using decision tree, random forest and moving average analysis. The accuracy of the models was assessed by calculating overall accuracy of prediction, sensitivity, specificity and Mathew's correlation coefficient. Random forest correctly classified the analogues into permeable and impermeable with an accuracy of 92.06%. A decision tree was also employed for determining the importance of various molecular descriptors. The decision tree learned the information from the input data with an accuracy of 96.82% and correctly predicted the cross-validated 10 fold data with accuracy up to 84.12%. Accuracy of prediction of proposed moving average analysis models was found to be 96.22% and 90.09%.