ABSTRACT
Recently we developed a model for prediction of pH-dependent aqueous solubility of drugs and drug like molecules. In the present work, the model was applied on a series of novel Histone Deacetylases (HDAC) inhibitors discovered at TopoTarget. The applicability of our model was evaluated on the series of HDAC inhibitors by use of Self-Organizing Maps (SOM) and 2D-projection of the HDAC inhibitors on the chemical space of the training data set of the artificial neural network (ANN) module. The model was refined for the particular chemical space of interest, which led to two modifications in the training data set of the ANN. The performance of the original and the two modified versions of the model were evaluated against the commercial software from Simulations-plus and pH-dependent solubility measurements for representative compounds of the series. The results of the evaluation indicate that one can develop models that are more accurate in predicting differences in the solubility of structurally very similar compounds than models that have been trained on structurally unbiased, diverse data sets. Such 'tailor-made' models have the potential to become trustworthy enough to replace time-consuming and expensive medium- and high-throughput solubility experiments by providing results of similar or even better quality.