Prediction of human intestinal absorption of drugs by using support vector machine

A set of molecular descriptors,including electronic descriptors,tpological descriptors,geometric descriptors and molecular shape indices,were calculated to characterize the structural and physicochemical properties for 230 chemical compounds.Support Vector Machine (SVM)classification method was employed to predict human intestinal absorption of molecules.Five-fold cross-validation methold was used to optimize the SVM model and genetic algorthm was used in variable selection,which reduced the number of molecular descriptors from 102 to 47.Five-fold cross-validation method and an independent evaluation set method were used to test SVM modle,where the training sets were effectively and evenly chosen in the descriptors space by clustering based on their chemical similarity,and both of the test methods ave consistent results.Our work suggests that a proper chioice of training set for 5-fold fross-validation methold or the independent test method can improve the efficiency of SVM model building.