This phenomenon, termed polypharmacology, is thought to be one critical cause of adverse drug effects ( Hopkins, 2008). In other cases, one drug has been shown to affect multiple targets from different protein families ( MacDonald et al, 2006 Paolini et al, 2006). For example, multiple structurally different compounds have been shown to bind the same protein or express similar biological activities ( Eckert and Bajorath, 2007 Young et al, 2008). Large-scale data sets of compound–protein interactions (CPIs) are being collected, and chemical genomics studies have shown that patterns of protein–ligand interactions are too diverse to be understood as simple one-to-one events. Recently, chemical genomics has emerged as a promising area of research applicable to exploration of novel bioactive molecules, and researchers are currently striving toward the identification of all possible ligands for all target protein families ( Wang et al, 2009).
Chemical space is as vast as the diversity of biological systems, and the vastness of the two domains creates difficulty in comprehensive understanding of the interface between chemical space and biological systems ( Lipinski and Hopkins, 2004 Renner et al, 2009). The set of all possible small organic molecules, referred to as chemical space, has been estimated to consist of more than 10 60 compounds ( Dobson, 2004). Unfortunately, the number of well-characterized chemical probes is highly limited, which has bottlenecked their wide range of application. In particular, perturbations by chemical probes provide broader applications not only for analysis of complex systems but also for intentional manipulations of these systems, e.g., a medicine is a small molecule designed for the purpose of clinical therapy that can actively manipulate biological systems from disordered to well-ordered states. The results of this study indicate that data derived from chemical genomics can be highly useful for exploring chemical space, and this systems biology perspective could accelerate drug discovery processes.Įxperimental perturbations of biological systems, such as genetic mutation and chemical exposure, have been used as powerful approaches to deepen our systems-level understanding of biological processes and to discover unprecedented biological phenomena ( Lehár et al, 2008). These novel compounds were not identified by existing computational ligand-screening methods in comparative studies. Through a machine-learning technique that uses multiple CPIs, we have successfully identified novel lead compounds for two pharmaceutically important protein families, G-protein-coupled receptors and protein kinases. Herein, we demonstrate that machine learning of multiple CPIs can not only assess drug polypharmacology but can also efficiently identify novel bioactive scaffold-hopping compounds. Although new target proteins for known drugs have recently been identified through mining of CPI databases, using these resources to identify novel ligands remains unexplored. For this purpose, the emerging field of chemical genomics is currently focused on accumulating large assay data sets describing compound–protein interactions (CPIs). The discovery of novel bioactive molecules advances our systems-level understanding of biological processes and is crucial for innovation in drug development.
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