Earlier (Kireeva et al. Mol. Inf. 2012, 31, 301-312), we demonstrated that generative topographic mapping (GTM) can be efficiently used both for data visualization and building of classification models in the initial D-dimensional space of molecular descriptors. Here, we describe the modeling in two-dimensional latent space for the four classes of the BioPharmaceutics Drug Disposition Classification System (BDDCS) involving VolSurf descriptors. Three new definitions of the applicability domain (AD) of models have been suggested: one class-independent AD which considers the GTM likelihood and two class-dependent ADs considering respectively, either the predominant class in a given node of the map or informational entropy. The class entropy AD was found to be the most efficient for the BDDCS modeling. The predominant class AD can be directly visualized on GTM maps, which helps the interpretation of the model.

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We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.