ESTIMATION OF STANDARD ENTHALPY OF FORMATION OF ALKANES IN GASEOUS STATE BY CALCULATING SIZE, STRUCTURAL AND ELECTRONIC PARAMETERS IN THE MOLECULES

A quantitative analysis is made on the correlation ship of thermodynamic property, i.e., standard enthalpy of formation (ΔH f0) with Kier’s molecular connectivity index(1Xv),vander waal’s volume (Vw) electrotopological state index (E) and refractotopological state index (R) in gaseous state of alkanes. The regression analysis reveals a significant linear correlation of standard enthalpy of formation (ΔH f0) with 1Xv, Vw, E and R. The equations obtained by regression analysis may be used to estimate standard enthalpy of formation (ΔH f0) of alkanes in gaseous state.

Simple graph-theoretical model for flavonoid binding to P-glycoprotein / Jednostavan graf-teorijski model vezivanja flavonoida za P-glikoprotein / Jednostavan graf-teorijski model vezivanja flavonoida za P-glikoprotein

Three sets of flavonoid derivatives (N=32, 40, and 74) and logarithms of their dissociation constants (log Kd) that describe flavonoid affinity toward P-glycoprotein were modelled using six connectivity indices. The best results were obtained with the zero-order valence molecular connectivity index (0χv) for all three sets. Standard errors of the calibration models were around 0.3, and of the constants from the test sets even a little lower, 0.22 and 0.24. Despite using only one descriptor, our model proved better in internal (cross-validation) and especially in external (test set) statistics than much more demanding methods used in previous 3D QSAR modelling.

Neural Network Model for Predicting Anticancer Activity of Pyridopyrimidines Derivatives

Molecular structures of pyridopyrimidines derivatives as known as dihydrofolate reductase (DHFR) inhibitors were investigated by using the neural network method. Based on the molecular connectivity, molecular connectivity index and molecular electronegativity distance vectors of 32 pyridopyrimidine derivatives were obtained. Among these parameters, three optimized structural parameters 1χ3χ and M17 as the input neurons of the artificial neural network were selected by step-wise regression. Then a 3:4:1 network architecture was employed and a satisfying neural network model for predicting anticancer activity (lg1/C) was constructed with the back-propagation (BP) algorithm. The total correlation coefficient R and the standard deviation S were 0.925 and 0.336 respectively that showed significantly nonlinear relationships between lg1/C and three structural parameters. It was concluded that the predictions of BP neural network are better than those of methods in the literatures.

Stability prediction of Cu2+, Ni2+ and Zn2+ N-salicylidene-aminoacidato complexes by models based on connectivity index 3 χ v

This paper presents models for the estimation of stability constants (K 1 and β 2) of nickel(II), copper(II) and zinc(II) mono- and bis-complexes with 5 Schiff bases (salicylideneglycine, salicylidenealanine, salicylideneserine, salicylidenephenylalanine, and salicylidenetyrosine). The models were based on the molecular-graph theory and valence molecular connectivity index of the 3rd order, 3χv , derived from it. Univariate linear models were developed for each metal separately, while in the common models for two and three metals, the indicator variable, In, was introduced. The standard error of models for the log K 1 constant was less than 0.12, while for log β 2 models, the S.E. did not exceed 0.14.

Research on Calculation of Molecular Connectivity Index

Compared to other topological indices, molecular connectivity index has good structural selectivity and correlation.According to the molecule’s 2D and 3D topology file, we conducted research on the mol2 format file to consider how to convert the ASCII file into the adjacency matrix. Based on the adjacency matrix, we analyzed the relevant algorithm to calculate the molecules’ molecular connectivity index through adjacency matrix. As a molecular descriptor, the molecular connectivity index can be used in QSAR.