首页> 中文期刊> 《结构化学》 >QSAR Study of the Action Strength of DOM of Phenyl-isopropyl-amine Dopes Using MLR and BP-ANN

QSAR Study of the Action Strength of DOM of Phenyl-isopropyl-amine Dopes Using MLR and BP-ANN

         

摘要

Based on Hall et al.electrotopological state indices (EK) of atom types,two quantitative structure-activity relationship (QSAR) models were developed to estimate and predict the action strength (W) of DoM (dimethoxy-methyl-amphetamine) for 18 phenyl-isopropyl-amine dopes (PPAD) through linear method (multiple linear regression,MLR) and non-linear method (Back propagation artificial neural network,BP-ANN).On the basis of Ek,the optimal three-parameter (E14,E9,E7) QSAR model of W for 18 PPAD was constructed.The traditional correlation coefficient (R2) and cross-validation correlation coefficient (Rcv2) are 0.878 and 0.815,respectively.The result demonstrates that the model is highly reliable (from the point of view of statistics) and has good predictive ability by using R2,Rcy2,VIF,FIT,AIC and F tests.Form the three parameters of the model,it is known that the dominant influence factors of inhibited activity are the molecular structure fragments:=CH-(secondary carbon),=C< (tertiary carbon atom) in aromatic ring and-O-(phenol ether bond).The results showed that the structure parameters E14,E9 and E7 have good rationality and efficiency for the W of phenyl-isopropyl-amine dope (PPAD) analogues.A BP-ANN with 3-3-1 architecture was generated by using three electrotopological state index descriptors (E14,E9,E7) appearing in the MLR model,the above descriptors were inputs and its output was action strength (W).The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with R2 and Rcy2 of leave-one-ont (LOO) to be 0.995 and 0.994,respectively.The regression method gave support to the neural network with physical explanation,which offers a more accurate model for QSAR.Those models can be used in the rational design of higher stimulating extent PPAD,which provide meaningful reference information to improve the detection methods of PPAD.

著录项

  • 来源
    《结构化学》 |2017年第10期|1720-1728|共9页
  • 作者

    WANG Chao; FENG Chang-Jun;

  • 作者单位

    School of Chemistry & Chemical Engineering,Xuzhou Institute of Technology, Xuzhou, Jiangsu 221111, China;

    School of Chemistry & Chemical Engineering,Xuzhou Institute of Technology, Xuzhou, Jiangsu 221111, China;

  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 中文文献
  • 外文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号