Abstract
Dark chemical matter compounds are small molecules that have been recently identified as highly potent and selective hits. 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. To this purpose, Wassermann et al. (2015) have described the application of 2D descriptors to characterize dark chemical matter. However, their definition was based on the number of reported positive assays rather than 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. Here, we propose an alternative classification of active molecules that is based on the number of known targets. 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. In this study, black and inactive compounds are found to have not only higher solubility, but also a higher number of chiral centers, sp3 carbon atoms and aliphatic rings. On the contrary, white compounds contain a higher number of double bonds and fused aromatic rings. Therefore, the design of a screening compound library should consider these molecular properties in order to achieve target selectivity or polypharmacology. 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. On the other side, ligands that hit kinases, proteases, and ion channels bind to GPCRs more likely than to other target classes. 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. Additionally, synergistic effects may be obtained by library design toward polypharmacology.
Highlights
Drug discovery for a specific target is a long process that starts from hit finding: in the past high throughput screening (HTS) of huge compound libraries was the most common process in pharmaceutical companies
Molecular Connectivity Predefines Polypharmacology precludes the discovery of new compounds, as the chemical space is much wider and the use of limited knowledge makes the hit discovery challenging (Dobson, 2004; Reymond, 2015)
We propose an alternative classification of active molecules, on the base of the number of targets they hit, in order to investigate target selectivity and/or polypharmacology in the early phase of the drug discovery process
Summary
Drug discovery for a specific target is a long process that starts from hit finding: in the past high throughput screening (HTS) of huge compound libraries was the most common process in pharmaceutical companies. The chemical space that the HTS can reach is restricted to the molecules that were previously synthesized and included in the screened library. Molecular Connectivity Predefines Polypharmacology precludes the discovery of new compounds, as the chemical space is much wider and the use of limited knowledge makes the hit discovery challenging (Dobson, 2004; Reymond, 2015). To overcome these disadvantages, computational techniques can be applied in order to speed up the process of drug design and to perform de novo drug design. Fragment-based and relatively small focused libraries have found great success: a wider chemical space is covered by virtually assembling many different building blocks as in combinatorial synthesis (Chevillard and Kolb, 2015; Reymond, 2015) or by building compounds directly starting from the structure complex with the first fragment (Srinivas Reddy et al, 2013)
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