Nitroaromatics are typical toxic organic pollutants and are ubiquitous in environment with diverse structures. They are widely used in many industries and formed during many natural and anthropogenic processes. Most o...Nitroaromatics are typical toxic organic pollutants and are ubiquitous in environment with diverse structures. They are widely used in many industries and formed during many natural and anthropogenic processes. Most of these pollutants are potentially carcinogenic and the as-sessment and prediction of the mutagenicity of nitroaromatics are of great interest. In this paper the structure-mutagenicity relationships of 219 nitroaromatics are investigated by molecular or-bital theory based classic structure-activity relationships and comparative molecular field analysis (CoMFA). A comparison is undertaken in respect of the interpretation of mechanism and predic-tive ability for these two categories of QSAR approaches and highly predictive QSAR models have been developed.展开更多
基金the National Key Project of Basic Research(Grant No.2003CB415002)National Natural Science Foundation of China(Grant No.20177008)+2 种基金EU International Scientific Cooperative Project(Grant No.ICA4-CT-2001-10039)863 High Tech.Project of China(Grant No.2001AA640601-4)Top 6 Talents Training Program of Jiangsu Province and Innovative Young Talents Program.
文摘Nitroaromatics are typical toxic organic pollutants and are ubiquitous in environment with diverse structures. They are widely used in many industries and formed during many natural and anthropogenic processes. Most of these pollutants are potentially carcinogenic and the as-sessment and prediction of the mutagenicity of nitroaromatics are of great interest. In this paper the structure-mutagenicity relationships of 219 nitroaromatics are investigated by molecular or-bital theory based classic structure-activity relationships and comparative molecular field analysis (CoMFA). A comparison is undertaken in respect of the interpretation of mechanism and predic-tive ability for these two categories of QSAR approaches and highly predictive QSAR models have been developed.
