J Comput Chem 2008 Jan;29(2):266-74

Chemistry Department, Shiraz University, Shiraz, Iran.

The usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the O-methylation kinetic of substituted phenols has been investigated. QTMS theory produces for each molecule a matrix of descriptors, containing bond (or structure) information in one dimension and electronic effects in another dimension, instead of other methods producing a vector of descriptors for each molecule. A collection of chemometrics tools including principal component analysis (PCA), partial least squares (PLS), and genetic algorithms (GA) were used to model the structure-kinetic data. PCA separated the bond and descriptor effects, and PLS modeled the effects of these parameters on the rate constant data, and GA selected the most relevant subset of variables. The model performances were validated by both cross-validation and external validation. The results indicated that the proposed models could explain about 95% of variances in the rate constant data. The significant effects of variables on the reaction kinetic were identified by calculating variable important in projection (VIP). It was found that the rate constant of esterification of phenols is highly influenced by the electronic properties of the C2--C1--O--H fragment of the parent molecule. Indeed, the C2--X and C4--X bonds (corresponding to ortho and para substituents) were found as highly influential parameters. All of the eight calculated QTMS indices were found significant however, lambda1, lambda2, lambda3, epsilon, and K(r) were detected as highly influential parameters.

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January 2008

rate constant

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highly influential

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qtms indices

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constant data

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molecular similarity

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substituted phenols

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influential parameters

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topological molecular

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quantum topological

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effects

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selected relevant

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relevant subset

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proposed models

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parameters rate

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models explain

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data selected

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variables model

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validated cross-validation

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cross-validation external

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