Modeling of Gibbs Energy of Formation of Organic Compounds by Linear and Nonlinear Methods

Abstract

Two quantitative models for the prediction of the Gibbs energy of formation (DeltaGf degrees ) of 177 organic compounds were developed. These molecules contain elements such as H, C, N, O, F, S, Cl, and Br, with the molecular weight in the range of 16.04-202.25. The molecules were represented by six selected 2D-structure descriptors. At first, the complex relationship between DeltaGf degrees and the six selected input descriptors was depicted by a two-dimensional Kohonen's self-organizing neural network (KohNN) map; on the basis of the KohNN map, the whole data set was split into a training set consisting of 130 compounds and a test set (or a validation set and a test set) including 47 compounds. Then, DeltaGf degrees was predicted using a multilinear regression (MLR) analysis and a back-propagation (BPG) neural network. For 177 organic compounds, root-mean-square deviations of 17.8 and 15.4 kcal mol-1 were achieved by MLR and the BPG neural network, respectively.

Estimation of enthalpies of formation of organometallic compounds from their molecular structures

“…Some QSPR models using topological descriptors to describe the standard Gibbs energy of formation of alkanes have been reported [13,14]. More recently, the D f G 0 values of a set of 177 organic compounds have been analyzed with six descriptors, by linear and nonlinear methods, giving root-mean-square deviations of 75 and 65 kJ/ mol, respectively [15]. In the present paper, we apply the QSPR methodology to predict gas-phase enthalpies of formation of organometallic compounds.…”