On variable Zagreb indices

A novel variant of the Zagreb indices introduced in 1972, called the variable Zagreb indices, is presented. Their performance in the QSPR modeling of lower (C(3)-C(8)) alkane boiling points is compared with the original Zagreb indices and their modifications. The variable Zagreb indices produce better single-descriptor models and participate in better two- and three-descriptor models

Estimation of Stablity Constants of Mixed Amino Acid Complexes with Copper(II) from Topological Indices

Stability constants of binary and ternary complexes of five naturali occurring amino acids (Gly, Ala, Val, Phe, Tyr) with copper(II) were correlated with the Wiener (W) and the valence-connectivity indices (χ) of their ML2 complexes, yielding the correlation coefficient r = 0.931-0.997 and the root-mean-square (rms) deviation of the measured and estimated values in the range of 0.013 to 0.065 log K units. Multiple regression of 27 second ligation constants of binary and ternary complexes of 10 amino acids (Gly, Ala, Val, Leu, Ser, Thr, Met, Phe, Tyr, Trp) on topological indices of various molecular species (L, ML, ML2) yielded R = 0.696-0.756 and rms = 0.17-0.19 log K2 units. The result is essentially better than the results obtained by the mechanistic method of M. Tabata and M. Tanaka (see Ref. 8) on the same set of data (rms = 0.39 log K2 units). The method appeared to be sensitive to the amino acid ability of additional (apical) coordination, but not very sensitive to other structural properties (aromaticity, polarity). Among the models used, the best results were obtained with the third-order valence-connectivity index (3χv) calculated from the formula of complex compounds (ML and ML2)

The Wiener Index: Development and Applications

The definitions and methods of computing the Wiener index are reviewed. It is pointed out that the Wiener index is a useful topological index in the structure-property relationship because it is a measure of the compactness of a molecule in terms of its structural chracteristics, such as branching and cyclicity. A comparative study between the Wiener index and several of the commonly used topological indices in the structure-boiling point relationship revealed that the Wiener index is, in this case, rather inferior to most indices, a result that has been observed by other authors as well. New developments, such as an extension of the Wiener index to its three-dimensional version are also mentioned

On the Concept of a Chemical Model

The concepts of model and modeling are discussed. Various classifications of models are given. It is pointed out that the progress of science in general and chemistry in particular is based on models and modeling since no better tools have yet been found to study Nature

Estimation of Stablity Constants of Mixed Amino Acid Complexes with Copper(II) from Topological Indices

Stability constants of binary and ternary complexes of five naturali occurring amino acids (Gly, Ala, Val, Phe, Tyr) with copper(II) were correlated with the Wiener (W) and the valence-connectivity indices (χ) of their ML2 complexes, yielding the correlation coefficient r = 0.931-0.997 and the root-mean-square (rms) deviation of the measured and estimated values in the range of 0.013 to 0.065 log K units. Multiple regression of 27 second ligation constants of binary and ternary complexes of 10 amino acids (Gly, Ala, Val, Leu, Ser, Thr, Met, Phe, Tyr, Trp) on topological indices of various molecular species (L, ML, ML2) yielded R = 0.696-0.756 and rms = 0.17-0.19 log K2 units. The result is essentially better than the results obtained by the mechanistic method of M. Tabata and M. Tanaka (see Ref. 8) on the same set of data (rms = 0.39 log K2 units). The method appeared to be sensitive to the amino acid ability of additional (apical) coordination, but not very sensitive to other structural properties (aromaticity, polarity). Among the models used, the best results were obtained with the third-order valence-connectivity index (3χv) calculated from the formula of complex compounds (ML and ML2)