文献类型：期刊文献

英文题名：Estimation of ADME Properties with Substructure Pattern Recognition

作者：Shen, Jie[1];Cheng, Feixiong[1];Xu, You[1];Li, Weihua[1];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Sch Pharm, Dept Pharmaceut Sci, Shanghai 200237, Peoples R China

年份：2010

卷号：50

期号：6

起止页码：1034

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20102613048380);WOS:【SCI-EXPANDED(收录号:WOS:000279069800007)】；

基金：We thank Mr. Lian Duan for discussions with the technique details of this work. This work was supported by the Program for New Century Excellent Talents in University (grant no. NCET-08-0774), the 863 High-Tech Project (grant no. 2006AA020404), the 111 Project (grant no. B07023), the National S&T Major Project of China (grant no. 2009ZX09501-001), and the Shanghai Natural Scientific Foundation (grant no. 10ZR1407000).

语种：英文

外文关键词：Support vector machines - Chemical analysis

摘要：Over the past decade, absorption, distribution, metabolism, and excretion (ADME) property evaluation has become one of the most important issues in the process of drug discovery and development. Since in vivo and in vitro evaluations are costly and laborious, in silico techniques had been widely used to estimate ADME properties of chemical compounds. Traditional prediction methods usually try to build a functional relationship between a set of molecular descriptors and a given ADME property. Although traditional methods have been successfully used in many cases, the accuracy and efficiency of molecular descriptors must be concerned. Herein, we report a new classification method based on substructure pattern recognition, in which each molecule is represented as a substructure pattern fingerprint based on a predefined substructure dictionary, and then a support vector machine (SVM) algorithm is applied to build the prediction model. Therefore, a direct connection between substructures and molecular properties is built. The most important substructure patterns can be identified via the information gain analysis, which could help to interpret the models from a medicinal chemistry perspective. Afterward, this method was verified with two data sets, one for blood-brain barrier (BBB) penetration and the other for human intestinal absorption (HIA). The results demonstrated that the overall predictive accuracies of the best HIA model for the training and test sets were 98.5 and 98.8%, and the overall predictive accuracies of the best BBB model for the training and test sets were 98.8 and 98.4%, which confirmed the reliability of our method. In the additional validations, the predictive accuracies were 94 and 69.5% for the HIA and the BBB models, respectively. Moreover, some of the representative key substructure patterns which significantly correlated with the HIA and BBB penetration properties were also presented.