In silico prediction of central nervous system activity of compounds. Identification of potential pharmacophores by the TOPS-MODE approach

The central nervous system (CNS) activity has been investigated by using a topological substructural molecular approach (TOPS-MODE). A discriminant analysis to classify CNS and non-CNS drugs was developed on a data set (302 compounds) of great structural variability where more than 81% (247/302) were well classified. Randic's orthogonalization procedures was carried out to allow the interpretation of the model and to avoid the collinearity among descriptors. The discriminant model was assessed by a leave-n-out (when n varies from 2 to 20) cross-validation procedure (79.94% of correct classification), an external prediction set composed by 78 CNS/non-CNS drugs (80.77% of correct classification) and a 5-fold full cross-validation (removing 78 compounds in each cycle, 80.00% of good classification). With this methodology was demonstrated that the hydrophobicity increase the CNS activity, while the dipole moment and the polar surface area decrease it; evidencing the capacity of the TOPS-MODE descriptors to estimate CNS activity for new drug candidates. The structural contributions to the CNS activity for two compounds are presented on the basis of fragment contributions. The model has also been able to identify potential structural pharmacophore, showing its possibilities in the lead generation and optimization processes.