Visualization of Molecular Selectivity and Structure Generation for Selective Dopamine Inhibitors

Activity landscapes were used in combination with atom colourings for the visualization of molecular selectivity. Multiple inhibitory activities in the dopamine family were selected in order to derive its molecular selectivity. All molecular structures were mapped in 2D chemical space by preserving the relative distance between any pair of molecules using multidimensional scaling. The values for the inhibitory activity against each dopamine isoenzyme (D2, D3, and D4) were added independently to the data points in 2D chemical space. Three activity landscapes were generated after carrying out colour-graded interpolation between the data points. The activity landscapes facilitated the detection of three specific active regions and the corresponding specific inhibitors for D2, D3, and D4 isoenzymes. Three support vector regression models were separately constructed using extended connectivity fingerprint descriptors and each inhibitory data set. By applying the atom scores along with the graded-colours to the specific inhibitors, the molecular selectivity differentiating each dopamine isoenzyme could be understood visually. Furthermore, the landscape technique was combined with structure generation to produce chemical structures that stay within the D3 specific active region.