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Prediction of Blood Brain Barrier Permeability of Ligands Using Sequential Floating Forward Selection and Support Vector Machine

  • Pooja Gupta
  • Utkarsh Raj
  • Pritish K. Varadwaj
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)

Abstract

Prediction of Blood Brain Barrier (BBB) permeability index has been established as an important criterion for CNS active drug molecules. Various experimental and in silico approaches were being used for the prediction BBB permeability with accuracy level fall within 80 % on test dataset (r2 = squared correlation coefficient; 0.65–0.91 derived from training set). In this study Sequential Floating Forward Selection (SFFS) feature selection method based Support Vector Machine (SVM) classification was carried out on a set of 453 chemically diverse compounds with known BBB permeability index. The prediction efficiency for the test set was found to be r2 = 0.95 for 369 compounds (within the applicability domain after excluding four activity outliers). Classification accuracies for permeable (BBB +ve) and non-permeable (BBB −ve) were 96.84 and 98.21 % respectively.

Keywords

Blood brain barrier In silico Support vector machine 

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Copyright information

© Springer India 2015

Authors and Affiliations

  • Pooja Gupta
    • 1
  • Utkarsh Raj
    • 1
  • Pritish K. Varadwaj
    • 1
  1. 1.Indian Institute of Information TechnologyAllahabadIndia

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