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Type: Artigo de periódico
Title: Structure-activity relationship (SAR) of substituted 17 alpha-acetoxyprogesterones studied with principal component analysis and neural networks using calculated physicochemical parameters
Author: Vendrame, R
Takahata, Y
Abstract: It was shown that the two different methods, the principal component analysis (PCA) and the neural network (NN), can classify oral progestational activity of substituted 17 alpha-acetoxyprogesterones into two categories, high active and low active, using only calculated molecular properties. The two methods can predict the category of each molecule with a fairly high percentage of success. The NN work was slightly better than the PCA in the prediction of the category. Ionization potential, molecular hardness, net atomic charges, frontier indices were found to be useful parameters for the classification of the compounds. (C) 1999 Elsevier Science B.V. All rights reserved.
Subject: structure-activity relationship
progestational activity
17 alpha-acetoxyprogesterones
principal component analysis
neural network
Country: Holanda
Editor: Elsevier Science Bv
Rights: fechado
Date Issue: 1999
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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