Chem Biol Drug Des 2007 Nov;70(5):413-23

Department of Chemistry, Isfahan University of Technology, Isfahan 84154, Iran.

Three-way analyses of quantum topological molecular similarity descriptors were used for quantitative structure property relationship modeling of the acidity constant of some phenol derivatives. A three-way data was built for different molecules by constructing a data matrix for each molecule. The matrix was produced by considering different bonds in each molecule and different descriptors in each bond. The three-way models parallel factor analysis and N-way partial least squares, and two-way models including partial least squares were used for modeling structure-acidity relationships. Comparison of the results showed that the three-way arrays produced more predictive models with lower over-fitting. The bilinear partial least square model resulted in a biased estimation of acidity constant of prediction set with average relative error of prediction of 1.87%, whereas that obtained by parallel factor analysis and N-way partial least squares was near to zero (i.e. -0.41 and -0.33, respectively). Additionally, the three-way methods allowed investigating the significant impact of different bonds and different descriptors using leverages of the parallel factor analysis loadings.

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November 2007

acidity constant

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parallel factor

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factor analysis

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partial squares

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similarity descriptors

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modeling acidity

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molecular similarity

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n-way partial

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analysis n-way

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quantum topological

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topological molecular

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three-way

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models including

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additionally three-way

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squares two-way

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two-way models

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squares modeling

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structure-acidity relationships

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-041 -033

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modeling structure-acidity

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