Binding energies for alkane molecules on a carbon surface from gas-solid chromatography and molecular mechanics

Gas-solid chromatography was used to determine B(2s) (gas-solid virial coefficient) values for 12 alkanes (10 branched and 2 cyclic) interacting with a carbon powder (Carbopack B, Supelco). B(2s) values were determined by multiple size variant injections within the temperature range of 393 to 623 K with each alkane measured at 5 or 6 different temperatures. The temperature variations of the gas-solid virial coefficients were used to find the experimental adsorption energy or binding energy values (E( *)) for each alkane. A molecular mechanics based, rough-surface model was used to calculate the molecule-surface binding energy (E(cal)( *)) using augmented MM2 parameters. The surface model consisted of three parallel graphene layers with each layer containing 127 interconnected benzene rings and two separated nanostructures each containing 17 benzene rings arranged in a linear strip. As the parallel nanostructures are moved closer together, the surface roughness increases and molecule-surface interactions are enhanced. A comparison of the experimental and calculated binding energies showed excellent agreement with an average difference of 3.8%. Linear regressions of E( *) versus E(cal)( *) for the current data set and a combined current and prior alkane data set both gave excellent correlations. For the combined data set with 18 linear, branched and cyclic alkanes; a linear regression of E( *)=0.9848E(cal)( *) and r(2)=0.976 was obtained. The results indicate that alkane-surface binding energies may be calculated from MM2 parameters for some gas-solid systems.     

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.     

Estimation of aqueous solubility of chemical compounds using E-state indices.

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.