In this report, the blood brain barrier (BBB) permeability prediction is carried out using a decision tree. A recently published data set of 497 compounds is selected to develop the tree model. The developed model shows an accuracy of 87.66% for training set; 86.09% in the 10-fold cross-validation procedure and 87.93% for the test set. Some structural explanation of how our model describe the passage of molecules through the BBB is given. Moreover, a comparison with other approaches is carried out showing good behaviour of our method. Finally, we can say that, the present results could represent a useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.
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Blood-Brain Barrier Passage Prediction Using Decision Tree
Published: 16 October 2017 by MDPI in MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed. congress CHEMBIOMOL-03: Chem. Biol. & Med. Chem. Workshop, Rostock, Germany-Bilbao, Spain-Galveston, Texas, USA, 2017
Keywords: Blood-brain barrier; Classification tree; Molecular descriptor; Quantitative Structure-Activity Relationship; WEKA