Tutorial Developing Models for Solubility Prediction with 18 Topological Descriptors

2 Tutorial Developing Models for Solubility Prediction with 18 Topological Descriptors 

In order to develop a proper QSPR model for solubility prediction, the first task is to select appropriate input deseriptors that are highly correlated with solubility. Clearly, many factors influence solubility - to name but a few, the si2e of a molecule, the polarity of the molecule, and the ability of molecules to participate in hydrogen honding. For a large diverse data set, some indicators for describing the differences in the molecules are also important. 

a list of 1 5 descriptors was calculated for these purposes, as described below. The partition coefficient log P (calculated by a method based on the Gho.sc/Crip-pen approach [11]) (see also Chapter X, Section 1.1 in the Handbook) was calculated because it affects the solubility dramatically [17, 18]. All the other descriptors were calculated with the program PETRA (Parameter Estimation for the Treatment of Reactivity Applications) [28.  

These first components of the autocorrelation coefficient of the seven physicochemical properties were put together with the other 15 descriptors, providing 22 descriptors. Pairwise correlation analysis was then performed a descriptor was eliminated if the correlation coefficient was equal or higher than 0.90, and four descriptors (molecular weight, the number of carbon atoms, and the first component of the 2D autocorrelation coefficient for the atomic polarizability and n-charge) were removed. This left 18 descriptors. 

A data set can be split into a training set and a test set randomly or according to a specific rule. The 1293 compounds were divided into a training set of 741 compounds and a test set ot 552 compounds, based on their distribution in a K.NN map. From each occupied neuron, one compound was selected and taken into the training set, and the other compounds were put into the test set. This selection ensured that both the training set and the test set contained as much information as possible, and covered the chemical space as widely as possible. 

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