Abstract
Water solubility is an important characteristic of a chemical in many aspects. However experimental definition of the endpoint for all substances is impossible. In this study quantitative structure–property relationships (QSPRs) for negative logarithm of water solubility–logS (molL−1) are built up for five random splits into the sub-training set (≈55%), the calibration set (≈25%), and the test set (≈20%). Simplified molecular input-line entry system (SMILES) is used as the representation of the molecular structure. Optimal SMILES-based descriptors are calculated by means of the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral). These one-variable models for water solubility are characterized by the following average values of the statistical characteristics: nsub_train=725–763; ncalib=312–343; ntest=231–261; rsub_train2=0.9211±0.0028; rcalib2=0.9555±0.0045; rtest2=0.9365±0.0073; ssub_train=0.561±0.0086; scalib=0.453±0.0209; stest=0.520±0.0205. Thus, the reproducibility of statistical quality of suggested models for water solubility confirmed for five various splits.
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