%0 Journal Article
%T Back-propagation network improved by conjugate gradient based on genetic algorithm in QSAR study on endocrine disrupting chemicals
%A JI Li
%A WANG XiaoDong
%A YANG XuShu
%A LIU ShuShen
%A WANG LianSheng
%A <br>
%J 科学通报(英文版)
%D 2008
%I 
%X Since the complexity and structural diversity of man-made compounds are considered, quantitative structure-activity relationships (QSARs)-based fast screening approaches are urgently needed for the assessment of the potential risk of endocrine disrupting chemicals (EDCs). The artificial neural net-works (ANN) are capable of recognizing highly nonlinear relationships, so it will have a bright applica-tion prospect in building high-quality QSAR models. As a popular supervised training algorithm in ANN, back-propagation (BP) converges slowly and immerses in vibration frequently. In this paper, a research strategy that BP neural network was improved by conjugate gradient (CG) algorithm with a variable selection method based on genetic algorithm was applied to investigate the QSAR of EDCs. This re-sulted in a robust and highly predictive ANN model with R2 of 0.845 for the training set, q2pred of 0.81 and root-mean-square error (RMSE) of 0.688 for the test set. The result shows that our method can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.
%K quantitative structure-activity relationships (QSARs)
%K endocrine disrupting chemicals
%K artificial neural networks
%K back-propagation
%K conjugate gradient
%K genetic algorithm<br>化学药物
%K 内分泌
%K 人造神经网络
%K 遗传算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=DD6615BC9D2CFCE0B6F945E8D5314523&aid=63A2DAEDF47DB429C04A8B279E2A48C7&yid=67289AFF6305E306&vid=8E6AB9C3EBAAE921&iid=CA4FD0336C81A37A&sid=27746BCEEE58E9DC&eid=7C3A4C1EE6A45749&journal_id=1001-6538&journal_name=科学通报(英文版)&referenced_num=0&reference_num=26