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Int. J. Mol. Sci. 2008, 9(10), 1961-1976; doi:10.3390/ijms9101961

Prediction of Human Intestinal Absorption by GA Feature Selection and Support Vector Machine Regression

State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P.R. China
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Received: 2 July 2008 / Revised: 5 September 2008 / Accepted: 15 October 2008 / Published: 20 October 2008
(This article belongs to the Section Physical Chemistry, Theoretical and Computational Chemistry)
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Abstract

QSAR (Quantitative Structure Activity Relationships) models for the prediction of human intestinal absorption (HIA) were built with molecular descriptors calculated by ADRIANA.Code, Cerius2 and a combination of them. A dataset of 552 compounds covering a wide range of current drugs with experimental HIA values was investigated. A Genetic Algorithm feature selection method was applied to select proper descriptors. A Kohonen's self-organizing Neural Network (KohNN) map was used to split the whole dataset into a training set including 380 compounds and a test set consisting of 172 compounds. First, the six selected descriptors from ADRIANA.Code and the six selected descriptors from Cerius2 were used as the input descriptors for building quantitative models using Partial Least Square (PLS) analysis and Support Vector Machine (SVM) Regression. Then, another two models were built based on nine descriptors selected by a combination of ADRIANA.Code and Cerius2 descriptors using PLS and SVM, respectively. For the three SVM models, correlation coefficients (r) of 0.87, 0.89 and 0.88 were achieved; and standard deviations (s) of 10.98, 9.72 and 9.14 were obtained for the test set.
Keywords: Human intestinal absorption (HIA); Kohonen’s self-organizing Neural Network (KohNN); Support Vector Machine (SVM); Genetic Algorithm Feature Selection; Quantitative Structure Activity Relationships (QSAR) Human intestinal absorption (HIA); Kohonen’s self-organizing Neural Network (KohNN); Support Vector Machine (SVM); Genetic Algorithm Feature Selection; Quantitative Structure Activity Relationships (QSAR)
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Yan, A.; Wang, Z.; Cai, Z. Prediction of Human Intestinal Absorption by GA Feature Selection and Support Vector Machine Regression. Int. J. Mol. Sci. 2008, 9, 1961-1976.

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