Consequently, depending on the target protein family, appropriate screening libraries can be designed in order to minimize the likelihood of unwanted side effects early in the drug discovery process.
On the other side, ligands that hit kinases, proteases, and ion channels bind to GPCRs more likely than to other target classes. Furthermore, analysis of four main target classes (GPCRs, kinases, proteases, and ion channels) shows that GPCR ligands are more selective than the other classes, as the number of black compounds is higher in this target superfamily. Therefore, the design of a screening compound library should consider these molecular properties in order to achieve target selectivity or polypharmacology. On the contrary, white compounds contain a higher number of double bonds and fused aromatic rings. In this study, black and inactive compounds are found to have not only higher solubility, but also a higher number of chiral centers, sp 3 carbon atoms and aliphatic rings. We cluster molecules in four classes: black, gray, and white compounds are active on one, two to four, and more than four targets respectively, whilst inactive compounds are found to be inactive in the considered assays. Here, we propose an alternative classification of active molecules that is based on the number of known targets. As there might be multiple assays for one single target, the number of assays does not fully describe target selectivity. However, their definition was based on the number of reported positive assays rather than the number of known targets. (2015) have described the application of 2D descriptors to characterize dark chemical matter. For this reason, they constitute a promising class of possible candidates in the process of drug discovery and raise the interest of the scientific community.