Monteleone Stefania, Fuchs Julian E, Liedl Klaus R
Institute of General, Inorganic and Theoretical Chemistry, Center of Molecular Biosciences, University of InnsbruckInnsbruck, Austria.
Front Pharmacol. 2017 Aug 28;8:552. doi: 10.3389/fphar.2017.00552. eCollection 2017.
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, sp 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.
深色化学物质化合物是最近被鉴定为具有高效能和高选择性的小分子。因此，它们在药物发现过程中构成了一类有前景的潜在候选物，并引起了科学界的兴趣。为此，瓦瑟曼等人（2015年）描述了二维描述符在表征深色化学物质方面的应用。然而，它们的定义是基于已报道的阳性检测数量，而不是已知靶点的数量。由于一个单一靶点可能有多种检测方法，检测数量并不能完全描述靶点选择性。在这里，我们提出了一种基于已知靶点数量的活性分子分类方法。我们将分子分为四类：黑色、灰色和白色化合物分别对一个、两到四个以及四个以上靶点有活性，而无活性化合物在考虑的检测中被发现无活性。在本研究中，发现黑色和无活性化合物不仅具有更高的溶解度，而且具有更多的手性中心、sp碳原子和脂肪族环。相反，白色化合物含有更多的双键和稠合芳香环。因此，筛选化合物库的设计应考虑这些分子特性，以实现靶点选择性或多药理学。此外，对四个主要靶点类别（GPCRs、激酶、蛋白酶和离子通道）的分析表明，GPCR配体比其他类别更具选择性，因为在这个靶点超家族中黑色化合物的数量更多。另一方面，作用于激酶、蛋白酶和离子通道的配体与GPCRs结合的可能性比与其他靶点类别结合的可能性更大。因此，根据目标蛋白家族，可以设计合适的筛选库，以便在药物发现过程的早期将不必要的副作用的可能性降至最低。此外，通过针对多药理学的库设计可能会获得协同效应。
