2D-QSAR Using MLR and 3D-QSAR Using CoMSIA Studies on the Toxicity of Aromatic Hydrocarbons on Larval Sinonvaculina Constricta

• 作者: JIANG Mei LI Xiao-Lin SHEN Xin-Qiang WANG Cui-Hua WU Yang YU Hong-Xia
• 来源期刊: 结构化学
• 学科: 化学
• 关键词: 3D-QSAR CoMSIA 国土资源部 比较分子相似性指数分析 毒性 多环芳烃 幼虫 缢蛏
• 年，卷（期）: 2012,（3）
• 页码范围: 420-428
• 页数: 9页
• 分类号: O641.121|TQ450.11
• 语种: 英文

摘要

Aromatic hydrocarbons,one of the persistent organic pollutants（POPs）,has been usually found in mussels,accumulated for their hard mobility and activities in harbours and estuaries.In this study,based on the 96 hr-LC50 of 12 aromatic hydrocarbons with larval sinonvaculina constricta,three-dimensional quantitative structure-activity relationship（3D-QSAR） technique：comparative molecular similarity indices analysis（CoMSIA） and 2D-QSAR technique：multiple linear regression（MLR） were described to obtain more detailed insight into the structure-activity relationships between the molecular structure and bio-activity.The results show the MLR model based on density functional theory（DFT） calculation carried out at the B3LYP/6-311＊＊ level with Gaussian 03 program yielded a very good correlation with a coefficient squared R2 of 0.716 and a cross-validated Q2 of 0.874.The dipole moment and enthalpy,as the thermodynamic parameters,were two important factors influencing pLC50.Correspondingly,CoMSIA based on the partial least-squares（PLS） methodology with steric,electrostatic,hydrophobic,H-bond donor and acceptor fields contributing simultaneously were employed and the values of R2 and the cross validation with leave-One-Out（LOO） Q2LOO were 0.585 and 0.990,respectively,which reveals the structure features,such as the electronegative substituent（nitro-group）,hydrophobic groups（the benzene ring） and H-bond（nitro-group）,related to the toxicity.The results of 2D-QSAR employing MLR model and 3D-QSAR employing CoMSIA model provide the useful information for predicting the toxicity of other aromatic hydrocarbons by comparing the molecular structures of similar compounds.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	WANG Cui-Hua		1	0	0.0	0.0
2	JIANG Mei		1	0	0.0	0.0
3	LI Xiao-Lin		1	0	0.0	0.0
4	SHEN Xin-Qiang		1	0	0.0	0.0
5	YU Hong-Xia		1	0	0.0	0.0
6	WU Yang		1	0	0.0	0.0