文献类型：期刊文献

英文题名：In Silico Assessment of Chemical Biodegradability

作者：Cheng, Feixiong[1];Ikenaga, Yutaka[3];Zhou, Yadi[1];Yu, Yue[1];Li, Weihua[1];Shen, Jie[1];Du, Zheng[1];Chen, Lei[1];Xu, Congying[1];Liu, Guixia[1];Lee, Philip W.[1,2];Tang, Yun[1]

机构：[1]E China Univ Sci & Technol, Shanghai Key Lab New Drug Design, Sch Pharm, Shanghai 200237, Peoples R China;[2]Kyoto Univ, Grad Sch Agr, Sakyo Ku, Kyoto 6068502, Japan;[3]Natl Inst Technol & Evaluat NITE, Safety Assessment Div, Chem Management Ctr, Shibuya Ku, Tokyo 1510066, Japan

年份：2012

卷号：52

期号：3

起止页码：655

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20121414924459);WOS:【SCI-EXPANDED(收录号:WOS:000301884400003)】；

基金：This work was supported by the Program for New Century Excellent Talents in University (Grant NCET-08-0774), the National Natural Science Foundation of China (Grant 21072059), the 111 Project (Grant B07023), the Shanghai Committee of Science and Technology (11DZ2260600), and the Fundamental Research Funds for the Central Universities (WY1113007).

语种：英文

外文关键词：Biodegradation - Nearest neighbor search - Organic chemicals - Decision trees - Indicators (chemical) - International trade

摘要：Biodegradation is the principal environmental dissipation process. Due to a lack of comprehensive experimental data, high study cost and time-consuming, in silico approaches for assessing the biodegradable profiles of chemicals are encouraged and is an active current research topic. Here we developed in silico methods to estimate chemical biodegradability in the environment. At first 1440 diverse compounds tested under the Japanese Ministry of International Trade and Industry (MITI) protocol were used. Four different methods, namely support vector machine, k-nearest neighbor, naive Bayes, and C4.5 decision tree, were used to build the combinatorial classification probability models of ready versus not ready biodegradability using physicochemical descriptors and fingerprints separately. The overall predictive accuracies of the best models were more than 80% for the external test set of 164 diverse compounds. Some privileged substructures were further identified for ready or not ready biodegradable chemicals by combining information gain and substructure fragment analysis. Moreover, 27 new predicted chemicals were selected for experimental assay through the Japanese MITI test protocols, which validated that all 27 compounds were predicted correctly. The predictive accuracies of our models outperform the commonly used software of the EPI Suite. Our study provided critical tools for early assessment of biodegradability of new organic chemicals in environmental hazard assessment.