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...

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

The multiple linear regression (MLR) was used to build the linear quantitative structure-property relationship (QSPR) model for the prediction of the molar diamagnetic susceptibility (χm) for 140 diverse organic compounds using the three significant descriptors calculated from the molecular structures alone and selected by stepwise regression method. Stepwise regression was employed to develop a regression equation based on 100 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. The stability of the proposed model was validated using Leave-One-Out cross-validation and randomization test. Application of the developed model to a testing set of 40 organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. By applying MLR method we can predict the test set (40 compounds) with Q 2 ext of 0.9894 and average root mean square error (RMSE) of 2.2550. The model applicability domain was always verified by the leverage approach in order to propose reliable predicted data. The prediction results are in good agreement with the experimental values.

https://www.pjchm.com/volume_2/issue_1/2_1_Paper_2.pdf

QSPR study of molar diamagnetic susceptibility of diverse organic compounds using multiple linear regression analysis

QSPR study of standard absolute entropies for gaseous organic compounds using novel molecular connectivity indexes and Ring parameter

For predicting the molar diamagnetic susceptibilities of organic compounds, a variable molecular connectivity indexmχ' and its converse index mχ'' based on adjacency matrix of molecular graphs and the variable atomic valence connectivity index δi' were proposed. The optimal values of parameters x, a, and y included in definition of δi', mχ' and mχ'' can be found by an optimization method. When x2.9, a1.10, and y0.36, a good five-parameter model for the molar diamagnetic susceptibilities can be constructed from 0χ', 1χ', 2χ', 1χ'' and 2χ'' by using the best subset regression analysis method. The correlation coefficient r, standard error s, and average absolute deviation of the multilinear regression (MLR) model are 0.9930, 4.96 cgs, and 3.74 cgs, respectively, for the 721 organic compounds (training set). The cross-validation by using the leave-one-out method demonstrates that the MLR model is highly reliable from the point of view of statistics. The average absolute deviation of predicted values of the molar diamagnetic susceptibility of another 360 organic compounds (test set) is 4.37 cgs for the MLR model. The results show that the current method is more effective than literature methods for estimating the molar diamagnetic susceptibility of an organic compound. The MLR method can provide an acceptable model for the prediction of the molar diamagnetic susceptibilities of organic compounds.

Lailong Mu

Papers

Improved QSPR Study of Diamagnetic Susceptibilities for Organic Compounds Using Two Novel Molecular Connectivity Indexes