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Exploring target-selectivity patterns of molecular scaffolds.

ABSTRACT: We investigate the question of whether target-selective molecular scaffolds can be identified on the basis of currently available compound activity data. Starting from a pool of 17745 public domain compounds with activity annotations for 433 human targets, we ultimately identify, through a selectivity classification and database-mining approach, 42 molecular scaffolds represented by multiple compounds that are highly selective for a particular target over one or more others. In many other cases, individual compounds representing unique scaffolds are target-selective. Hence, currently available public domain compound selectivity data are sparse. However, we also identify selectivity patterns that evolve around specific targets and are formed by multiple target-selective scaffolds. These scaffolds should provide interesting starting points for further chemical exploration.

SUBMITTER: Hu Y

PROVIDER: S-EPMC4007699 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Exploring target-selectivity patterns of molecular scaffolds.

Hu Ye Y Bajorath Jürgen J

ACS medicinal chemistry letters 20100201 2

We investigate the question of whether target-selective molecular scaffolds can be identified on the basis of currently available compound activity data. Starting from a pool of 17745 public domain compounds with activity annotations for 433 human targets, we ultimately identify, through a selectivity classification and database-mining approach, 42 molecular scaffolds represented by multiple compounds that are highly selective for a particular target over one or more others. In many other cases, ...[more]

PMID: 24900176

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Project description: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.

Dataset Information

Exploring target-selectivity patterns of molecular scaffolds.

Publications

Exploring target-selectivity patterns of molecular scaffolds.

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