A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Heterogeneous Fenton degradation of azodyes catalyzed by modified polyacrylonitrile fiber Fe complexes: QSPR (quantitative structure peorperty relationship) study

The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The multiple linear regression （MLR） methodwas employed todevelop the quantitative structure property relationship （QSPR） model equations for thedecoloration and mineralization of azodyes. Moreover, the predictive ability of the QSPR model equationswas assessed using Leave-one-out （LOO） and cross-validation （CV） methods. Additionally, the effect of Fe content of catalyst and the sodium chloride inwater on QSPR model equationswere also investigated. The results indicated that the heterogeneous photo-Fentondegradation of the azodyeswithdifferent structureswas conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for thedyedecoloration and mineralizationwere successfullydeveloped using MLR technique. MW/S （molecularweightdivided by the number of sulphonate groups） and N N=N （the number of azo linkage） are considered as the most importantdetermining factor for thedyedegradation and mineralization, and there is a significant negative correlation between MW/S or N N=N anddegradation percentage or total organic carbon （TOC） removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloridedid not alter the nature of the QSPR model equations.

Prediction of Molar Absorptivities of Color Reagents and Their Color Reactions with Yttrium by Artificial Neural Networks

The new topological indices A x1 A x3 suggested in our laboratories were applied to the study of structure property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.

Application of TLSER method in predicting the aqueous solubility and n-octanol/water partition coefficient of PCBs,PCDDs and PCDFs

The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-polychlorinated biphenyls(PCBs), polychlorinated dibenzodioxins and dibenzofurans(PCDDs and PCDFs). For each compound, five quantum parameters were calculated using AM1 semiempirical molecular orbital methods and used as structure descriptors: average molecular polarizability(α), energy of the lowest unoccupied molecular orbit( E _ LUMO ), energy of the highest occupied molecular orbit( E _ HOMO ), the most positive charge on a hydrogen atom( q _+), and the most negative atomic partial charge( q _-) in the solute molecule. Then standard independent variables in TLSER equation was extracted and two series of quantitative equations between these quantum parameters and aqueous solubility and n -octanol/water partition coefficient were obtained by stepwise multiple linear regression(MLR) method. The developed equations have both quite high accuracy and explicit meanings. And the cross-validation test illustrated the good predictive power and stability of the established models. The results showed that TLSER could be used as a promising approach in the estimation of partition and solubility properties of macromolecular chemicals, such as persistent organic pollutants.