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NEURAI NETWORKS IN Q. S. A. R STUDIES:Estimation and prediction of The anticonvuisant activities of thirty─eight cinnamamides
引用本文:HU Bu-chao, WANG Hong-Lun, GAO Guang-Jiang, LI Xu-wu(department of Nature product: Institute of Medicine Industry of shanxi XIAN710032). NEURAI NETWORKS IN Q. S. A. R STUDIES:Estimation and prediction of The anticonvuisant activities of thirty─eight cinnamamides[J]. 数理医药学杂志, 1999, 0(3)
作者姓名:HU Bu-chao   WANG Hong-Lun   GAO Guang-Jiang   LI Xu-wu(department of Nature product: Institute of Medicine Industry of shanxi XIAN710032)
摘    要:ltheory1.lprincipleoftheModifiedback-pr0pagation(MBP){'j1.2AIgorithmInthispapper.UsesM.B.PalgorithSupervisedLearn-ing.LeaningSignalC0nsists0ffeed-forwardandbackpropa-gati0n(Fig3).Alqorithm:lISigmoidfunctiony(x)=deIConnectionW',andQ,NInputtraimlngpattenSampleX,.andd:,,ftCalclate(coant)rea1ityValue0foatputY.VregulateWeightW,,W,,(l l)=W,,(l) V8.,01j-outputlayer:6'=y,(lwy,)(d,wy,)v.j-hiddenlayer:3j=O,(l-O,)=3kWjkkkisj(n l)layern0de.bN$O###At4oMBackto4StepContinuediterationuntilerr…


NEURAI NETWORKS IN Q. S. A. R STUDIES:Estimation and prediction of The anticonvuisantactivities of thirty─eight cinnamamides
HU Bu-chao, WANG Hong-Lun, GAO Guang-Jiang, LI Xu-wu. NEURAI NETWORKS IN Q. S. A. R STUDIES:Estimation and prediction of The anticonvuisantactivities of thirty─eight cinnamamides[J]. Journal of Mathematical Medicine, 1999, 0(3)
Authors:HU Bu-chao   WANG Hong-Lun   GAO Guang-Jiang   LI Xu-wu
Affiliation:department of Nature product: Institute of Medicine Industry of shanxi XIAN710032
Abstract:Neural Networks (NN ) Method was used to study The structure - activity relationship (S. A. R ) of cinnamamide derivatives. The relationship between biological activity (P. C) and thosc parameters such as the Partition coefficients(log p octanol/water) of the compounds Hammett 8 constants and steric parameter(M. R) of cinnamamides were investigated by the modified backpropagation (MBP) Neural Netwoks. The biological activity of cinnamamides derivatives thus estimated and predicted 100% fit with MLP Method. The resalts obtined by the developed MBPNN Method Seem to be better than those by Multivariate linear regression (MLR). The neural Networks Method might therefore be regarded as an excellent and effective chemometric Modelling technique for estim ating and predicting biological cativity on basic Q. S. A. R studies.
Keywords:Neural Networks (NN ) Cinnamamider Q. S. A. R
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