Quantitative Structure-Property Relationship (QSPR) models are useful in understanding how chemical structure relates to the physicochemical properties of natural and synthetic chemicals. In the present investigation the applicability of various topological indices are tested for the QSPR study on 24 aldehydes. The topological indices used for the QSPR analysis were Randic () (the first order molecular connectivity), Balaban (J), Wiener (W) and Harary (H) indices. In this study, the relationship between the topological indices to the thermal energy (Eth), heat capacity (Cv) and entropy(S) of 24 aldehydes are established. The thermodynamic properties are taken from HF level using the ab initio 6-31 G basis sets from the program package Gussian 98. For obtaining appropriate QSPR model we have used multiple linear regression (MLR) techniques and followed Back ward regression analysis. The results have shown that combining the three descriptors (J, W, ) could be used successfully for modeling and predicting the heat capacity (CV), two descriptors (J, 1) could be efficiently used for estimating the entropy (S) and one descriptors () could be predict the thermal energy of compounds.

http://ijmc.kashanu.ac.ir/article_15656_15cda36e3c4738750662d2bf376a5cbb.pdf

QSPR Modeling of Heat Capacity, Thermal Energy and Entropy of Aliphatic Aldehydes by using Topological Indices and MLR Method

The usefulness of five specific information-theoretical molecular descriptors was investigated for predicting the gas phase entropy of selected classes of acyclic and cyclic compounds. Among them, total information on atomic number (TIZ), graph vertex complexity (HV) and total information on bonds (TIBAT), considered together showed the best correlation along with a low standard deviation (r2 = 0.97, s = 21.14) with gas phase entropy values of 130 compounds. The multiple regression equation treating these three indices as independent variables was statistically highly significant which was evident from the F-statistics. In particular, very small difference between r2 and r2-pred values indicates that the regression model is not overfitted and is, therefore, suitable for prediction purposes. When truly used as a training set to predict (from regression equation) 40 additional compounds we get a very high correlation (r2 = 0.975), which remains almost identical (r2 = 0.97) for the combined data set of 170 compounds. The three indices appear to be useful descriptors producing correlation that remains stable with the change in the size of the data set. Also, the information-theoretical measures appear to capture an additive-cum-constitutive nature of gas phase entropy yielding an acceptable statistical fit.

Predicting gas phase entropy of select hydrocarbon classes through specific information-theoretical molecular descriptors.

A technique for constructing the correlation dependencies of the thermodynamic properties of similar compounds was proposed. This technique is based on the Taylor expansion in the supposed analytic relationship between properties. The constructed correlation dependencies were used to estimate the thermodynamic properties of the condensed bromides and iodides of hafnium HfGn (G = Br, I and n=1, 2, 3, 4) and the compounds formed in the PbO–SiO2 system. Standard formation enthalpy ΔH298o, entropy S298o, heat capacity Cp,298o and temperature dependencies Cp(T) at temperatures T=298k to 3000k were estimated. The standard estimation error of the thermodynamic properties of the compounds in the PbO–SiO2 system does not exceed 2% for all the estimated properties. For condensed halides, the standard estimation error was (1) S<8% for enthalpy, (2) S<5.6% for entropy, (3) S<4.6% for heat capacity.

Correlation model to estimate the thermodynamic properties

A norm index-based QSPR model to predict the standard absolute entropy of organic compounds in three phase states

To predict the standard absolute entropies of gaseous organic compounds, the variable molecular connectivity index (mχ′) and Ring parameter (H), based on adjacency matrix of molecular graphs, varia...

Hongmei He

Papers

Quantitative Structure–Property Relations (QSPRs) for Predicting the Standard Absolute Entropy (S298 K°) of Gaseous Organic Compounds