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.

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Under REACH legislation, alternative methods (in silico or in vitro) like QSAR (Quantitative Structure-Activity Relationships) models are expected to play a significant role. QSARs are based on the assumption that substances with similar chemical structures may have the same biological activities. However, identification of chemical classes could be problematic because chemicals often exhibit different chemical moieties, thereby confounding efforts to achieve a meaningful classification. This publication is focus on the notion of global model with the integration of a recent genetic algorithm for the generation of QSAR models. Starting from three datasets (EPAFHM, ECBHPV, AQUIRE), prediction of acute toxicity for fish (Pimephales promelas) with a global consensus model was carried out leading to very interesting statistics. The integration of the notion of Mode of Action was the second point of this study. A Bayesian classification associated to the genetic algorithm for consensus models was created leading to a good estimation of toxicity associated to derivatives with nonspecific MOA.

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The potential of quantile regression (QR) and quantile support vector machine regression (QSVMR) was analyzed for the definitions of quantitative structure-activity relationship (QSAR) models associated with a diverse set of chemicals toward a particular endpoint. This study focused on a specific sensitive endpoint (acute toxicity to algae) for which even a narcosis QSAR model is not actually clear. An initial dataset including more than 401 ecotoxicological data for one species of algae (Selenastrum capricornutum) was defined. This set corresponds to a large sample of chemicals ranging from classical organic chemicals to pesticides. From this original data set, the selection of the different subsets was made in terms of the notion of toxic ratio (TR), a parameter based on the ratio between predicted and experimental values. The robustness of QR and QSVMR to outliers was clearly observed, thus demonstrating that this approach represents a major interest for QSAR associated with a diverse set of chemicals. We focused particularly on descriptors related to molecular surface properties.

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