The importance of descriptor-based clusterization in QSAR models development: Tyrosine kinases inhibitors as a key study

Quantitative Structure Activity Relationship (QSAR) is a well known cheminformatic tool for the discovery of novel biologically active compounds. However, when large and heterogeneous datasets are mined, it is not possible to derive a QSAR equation able to predict in a satisfactory manner the activity of the compounds. Thus, QSAR models are often inadequate for virtual screening purpose. Herein we present a novel approach to multitarget classification QSAR models, useful to assess the selectivity profile of the tyrosine kinases inhibitors. A descriptor-based clusterization process was employed, that allowed the generation of models with high accuracies and independent from the chemical classification of the compounds (i.e. from the scaffold type). The herein proposed methodology can lead to QSAR models useful for virtual screening processes.