Many definitions of 'drug-like' compound properties have been published; based on the analysis of simple molecular properties of successful drugs. These are typically presented as rules that define acceptable boundaries for these properties. When a compound does not 'fit' within these boundaries then its properties differ from those of the majority of drugs, which could indicate a higher risk of poor pharmacokinetics or safety outcomes in vivo. Here, we review the strengths and weaknesses of these rules and note, in particular, that the overly rigid application of strict cut-off points can introduce artificial distinctions between similar compounds, running the risk of missing valuable opportunities. Alternatively, compounds can be ranked according to their similarity to marketed drugs using a continuous measure of drug-likeness. However, being similar to known drugs does not necessarily mean that a compound is more likely to become a drug and we demonstrate how a new approach, employing Bayesian methods, can be used to compare a set of successful drugs with a set of non-drug compounds to identify those properties that give the greatest distinction between the two sets, and hence the greatest increase in the likelihood of a compound becoming a successful drug. This analysis further illustrates that guidelines for drug-likeness might not be generally applicable across all compound and target classes or therapeutic indications. Therefore, it might be more appropriate to consider specific guidelines for drug-likeness that are project specific. © 2013 Elsevier Ltd.
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