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

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.

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