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WIREs Comput Mol Sci
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Druggability predictions: methods, limitations, and applications

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Abstract Pharmacological treatment with small organic molecules offers important medical, economic, and practical advantages over other therapeutic approaches. However, such small molecules can only elicit an effect when they bind to a biological component at the appropriate site and with sufficient affinity to modify its behavior. Druggability predictions assess the ability of a given binding site to host drug‐like organic molecules. Combined with information about the involvement of such component in a disease, druggability predictions can be used to prioritize therapeutic targets and are particularly useful when moving beyond the traditional target classes. In the last few years, significant progress has been made to understand the molecular basis of druggability, to compile datasets of targets with various degrees of druggability, and to develop a diverse set of computational prediction methods. These tools offer a better prospect for target‐based drug discovery. © 2012 John Wiley & Sons, Ltd. This article is categorized under: Computer and Information Science > Chemoinformatics

Protein targets of small‐molecule drugs, classified by protein type. Receptors, enzymes, and transporters are subdivided into protein families. (Created with data from Ref 8.) GPCRs, G‐protein coupled receptors; LG‐IC, ligand‐gated ion channels; RTK, receptor tyrosine kinases; Ig‐like, immunoglobulin‐like receptors; NR, nuclear receptors; VG‐IC, voltage‐gated ion channels.

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