Abstract
Abstract In the beginning there are 180 drug compounds with different human intestinal absorption (HIA) values obtained from literatures. From NCBI these 180 compound 3D structures are obtained. Before when starts any chemical compounds calculation, it is necessary to have them optimized. We use Gaussian09 in NCHC (National Center for High-performance Computing) with DFT, 6-31G via B3LYP energy levels to optimize those compounds. Discovery Studio is responsible to convey the coordinates to real 3D structures. Padel(Pharmaceutical Data Exploration Laboratory)-molecular descriptors is developed from National University of Singapore (NUS), is a powerful software contains 1875 molecular descriptors from 2D to 3D. And then we calculated 2D and 3D descriptors via Padel. WEKA is a data exploring machine learning soft ware developed via Wekato University, New Zealand. WEKA offers classification selecting attributes function. Feature selection is mainly based on selecting attributes function. The selecting methods are dominantly calculating via best-first evaluator with PSO (Particle Swarm Optimization) and EA (evolutionary algorithm) methods, another 5 algorithms are used to compensate whether there are some possible related to HIA features maybe being lost . SVM is used to classify and valid the results via choosing the proper parameters. There are three parameters important to classification: Cost (C), Gamma (γ) and epsilon (ε). Bad parameter chooses induces incorrect classification or overffiting results. In this research we have tried many series of parameters to obtain the best set validated via Pearson correlation coefficient and cross validate statistics. Classification and feature selection are used alternately until the ranges of selected features are not narrowed. By means of feature selection, the classification is getting better. Selecting features step by step its number of selected features (NFS) vary from 1875, 1104, 625, 280, 177, 98, 50 and 37. Using non validated statistics and 10-fold statistics, NFS=98 is the best predictive feature sets and its correlation coefficient R^2 is 0.88. That is these 98 molecular descriptors are highly correlated to intestinal absorption. Finally the testing task is completed via external validation. 13 drug compounds with different HIA values are used to the external validation. The q^2 is 0.729 and R^2 is 0.7536.