title: Quantitative structure-activity relationship prediction of blood-to-brain partitioning behavior using support vector machine.
abstract: In the present study a quantitative structure-activity relationship (QSAR) technique was developed to investigate the blood-to-brain barrier partitioning behavior (log BB) for various drugs and organic compounds. Important descriptors were selected by genetic algorithm-partial least square (GA-PLS) methods. Partial least squares (PLS) and support vector machine (SVM) methods were employed to construct linear and non-linear models, respectively. The results showed that, the log BB values calculated by SVM were in good agreement with the experimental data, and the performance of the SVM model was superior to the PLS model. The study provided a novel and effective method for predicting blood-to-brain barrier penetration of drugs, and disclosed that SVM can be used as a powerful chemometrics tool for QSAR studies.
authors: Department of Chemistry, Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran.
authors: Hassan
authors: H
authors: Golmohammadi
authors: Zahra
authors: Z
authors: Dashtbozorgi
authors: William E
authors: WE
authors: Acree
mesh_terms: D001812:Blood-Brain Barrier / Q000378:metabolism*; D016018:Least-Squares Analysis; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D004364:Pharmaceutical Preparations