Multi-way analysis of quantum topological molecular similarity descriptors for modeling acidity constant of some phenolic compounds.
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.
Esteki
Mahnaz
M
Department of Chemistry, Isfahan University of Technology, Isfahan 84154, Iran.
Hemmateenejad
Bahram
B
Khayamian
Taghi
T
Mohajeri
Afshan
A
D002138:Calibration; D006860:Hydrogen Bonding; D006863:Hydrogen-Ion Concentration; D008958:Models, Molecular; D008968:Molecular Conformation; D010636:Phenols / Q000737:chemistry*; D011789:Quantum Theory; D013329:Structure-Activity Relationship
D010636:Phenols