A stoichiometric approach to quantitative structure-property relationships (QSPR)

An unusual analogy between the quantitative structure-property relationships (QSPR), stoichiometry, chemical thermodynamics, and kinetics is presented. Namely, the conventional ordinary least-squares (OLS) QSPR analysis is modified so as to explicitly minimize the residuals of the species subject to a set of linear relations among the residuals. The ways the linear relations among the residuals are visualized and defined totally resemble the formalism of chemical stoichiometry and, therefore, were called isostructural reactions. It is further proved that the residuals may be uniquely partitioned into a sum of contributions associated with a set of isostructural reactions that have the same properties as the response reactions (RERs) previously deduced by us from chemical thermodynamics and kinetics. This finding is shown to be a useful tool for a deeper understanding of the QSPR. In particular, the isostructural RERs approach may be effectively used to detect the outliers.     

Novel topological index for research on structure-property relationships of complex organic compounds

Based on bonding parameters such as Yang's Electronegative Force Gauge Y(i), electronic number of valence layer Z(i), number of combined hydrogen atoms h(i), number of bonding electron b(i), and quantum number such as the highest main quantum number of valence layer n(i), a novel atomic valence delta(i) (Y) is defined and a novel topological index (1)chi(Y) is derived from the atomic valence. The atomic valence is defined as delta(i) (Y) = (Z(i) - h(i))b(i)/n(i) (2)Y(i), while the topological index is expressed as (1)chi(Y) summation operator (i,j=1) (m) (delta(i) (Y)delta(j) (Y))(-1/2). Subsequently, the index (1)chi(Y) is utilized to study the structure-property relationships of complex organic compounds. The results of correlativity showed that the index is highly and extensively correlated with such properties as solubility of phenyl chlorides, gas chromatographic retention index of alkoxyl silanes, and toxicity of heterocyclic nitrogen-containing compounds. Moreover, predicted values are quite consistent with experimental ones when the index is employed to predict the partition coefficient (log P) of fatty alcohols, phenyl chlorides, and barbitals. Compared to the topological indices reported in the literature, the universality and reliability of (1)chi(Y) to the properties of complex organic compounds have been distinctively improved, and its calculating process is simple and convenient.     

Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE)

The revised general solubility equation (GSE) is used along with four different methods including Huuskonen's artificial neural network (ANN) and three multiple linear regression (MLR) methods to estimate the aqueous solubility of a test set of the 21 pharmaceutically and environmentally interesting compounds. For the selected test sets, it is clear that the GSE and ANN predictions are more accurate than MLR methods. The GSE has the advantages of being simple and thermodynamically sound. The only two inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). No fitted parameters and no training data are used in the GSE, whereas other methods utilize a large number of parameters and require a training set. The GSE is also applied to a test set of 413 organic nonelectrolytes that were studied by Huuskonen. Although the GSE uses only two parameters and no training set, its average absolute errors is only 0.1 log units larger than that of the ANN, which requires many parameters and a large training set. The average absolute error AAE is 0.54 log units using the GSE and 0.43 log units using Huuskonen's ANN modeling. This study provides evidence for the GSE being a convenient and reliable method to predict aqueous solubilities of organic compounds.     

Evaluation of a quantitative structure-property relationship (QSPR) for predicting mid-visible refractive index of secondary organic aerosol (SOA)

In this work we describe and evaluate a simple scheme by which the refractive index (λ = 589 nm) of non-absorbing components common to secondary organic aerosols (SOA) may be predicted from molecular formula and density (g cm(-3)). The QSPR approach described is based on three parameters linked to refractive index-molecular polarizability, the ratio of mass density to molecular weight, and degree of unsaturation. After computing these quantities for a training set of 111 compounds common to atmospheric aerosols, multi-linear regression analysis was conducted to establish a quantitative relationship between the parameters and accepted value of refractive index. The resulting quantitative relationship can often estimate refractive index to ±0.01 when averaged across a variety of compound classes. A notable exception is for alcohols for which the model consistently underestimates refractive index. Homogenous internal mixtures can conceivably be addressed through use of either the volume or mole fraction mixing rules commonly used in the aerosol community. Predicted refractive indices reconstructed from chemical composition data presented in the literature generally agree with previous reports of SOA refractive index. Additionally, the predicted refractive indices lie near measured values we report for λ = 532 nm for SOA generated from vapors of α-pinene (R.I. 1.49-1.51) and toluene (R.I. 1.49-1.50). We envision the QSPR method may find use in reconstructing optical scattering of organic aerosols if mass composition data is known. Alternatively, the method described could be incorporated into in models of organic aerosol formation/phase partitioning to better constrain organic aerosol optical properties.     

Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology

An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided into a training set of 884 compounds and a randomly chosen test set of 413 compounds. The structural parameters in a 30-12-1 artificial neural network included 24 atom-type E-state indices and six other topological indices, and for the test set, a predictive r2 = 0.92 and s = 0.60 were achieved. With the same parameters the statistics in the multilinear regression were r2 = 0.88 and s = 0.71, respectively.     

Exploration of quantitative structure-property relationships (QSPR) for the design of new guanidinium ionic liquids

Computer-aided design of new guanidinium salts was explored and experimentally tested, en route to the discovery of new ionic liquids. Quantitative structure-property relationships were established to predict the mp of guanidinium salts of four different anionic families (Cl⁻, BPh₄⁻, Br⁻, and I⁻). Models were built with a data set of 101 salts and counterpropagation neural networks. Predictions for an independent test set were obtained with R²=0.815, and a fivefold cross-validation procedure yielded R²=0.742. Assisted by the models, six new guanidinium salts were prepared, and the measured melting properties were reasonably in accordance with the predictions. One of the new chloride salts is liquid at room temperature, and three tetraphenylborate salts have mp values lower than those previously available in the data set for that anion.     

Comparison of two methods for predicting aqueous solubility.

This study compares the solubility predictions of the two parameter general solubility equation (GSE) of Jain and Yalkowsky with the 171 parameter Klopman group contribution approach. Melting points and partition coefficients were obtained for each of the compounds from Klopman's test set. Using these two variables, the solubility of each compound was calculated by the GSE and compared to the values predicted by Klopman. Both methods give reasonable solubility predictions. The data of Klopman produced an average absolute error (AAE) of 0.71 and a root-mean-square error (RMSE) of 0.86, while the GSE had an AAE of 0.64 and a RMSE of 0.92.