No abstract

The Molecular Conformer Electron Topological (MCET) method was performed for the identification of the pharmacophore (Pha) group and predicting inhibitory activity of 42 flavonoid ligands on gamma-aminobutyric acid/benzodiazepine receptor complex (GABAA/BZR). In this method, Electron Topological Matrix (ETM) was used to visualize 3D structural descriptors. Multiple comparisons of ETM matrices for all flavonoid compounds allow us to define Pha-structure. Genetic algorithm (GA)-Partial Least-Squares (PLS) methods were performed to construct QSAR model and to select most important descriptors of the training set (32 compounds) and test set (10 compounds). The GA-PLS based model showed good results, q 2 = 0.808 and r 2 test = 0.775 with high internal and external validation. The developed model can help to understand the inhibitory mechanism.

According to the descriptors in the pharmacophore model, dividing molecules into training and test sets serves to create a good model. It is difficult to track the Local Reactive Descriptor (LRD) effect of the pharmacophore at each interaction point in the 3D metric system. A subset of clusters of atoms can correspond to all or part of the pharmacophore structure. In this study, the multidimensional system of the subset was reduced to a one-dimensional index and the Vector Fingerprint Functions (VFF) of the molecules were created. Models were established by dividing molecules with close and similar VFFs into training and test sets. Sub-clusters were examined for all molecules by applying the Genetic Algorithm (GA). The model was predicted using the Leave One Out-Cross Validation (LOO-CV) method and verified with an external test set. The statistical results of the model obtained according to the division in the new method we developed (Q2 = 0.604 and R2 = 0.760 for training-80 and external test-20 sets, respectively) were compared with random and manual division results.