Preprint: Considering the Impact of ‘Drug-like’ Properties on the Chance of Success
This paper was published in Drug Discovery Today 18(13-14), pp. 659-666 (DOI 10.1016/j.drudis.2013.02.008). In the paper we review the strengths and weaknesses of different definitions of ‘drug-like’ properties and measures of ‘drug-likeness.’ We propose an alternative metric the Relative Drug Likelihood (RDL) that identifies the properties with the greatest impact on a compound’s likelihood of success for a drug discovery objective.
Many definitions of ‘drug-like’ compound properties have been published, based on analysis of simple molecular properties of successful drugs. These are typically presented as rules that indicate when a compounds properties differ significantly from those of the majority of drugs, which may indicate a higher risk of poor outcomes for in vivo pharmacokinetics or safety. We review the strengths and weaknesses of these rules and note, in particular, that overly rigid application of hard cut-offs can introduce artificial distinctions between similar compounds and runs 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, utilising Bayesian methods, can be used to compare a set of successful drugs with a set of non-drug compounds in order to identify those properties whose values 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’ may not be generally applicable across all compound and target classes or therapeutic indications. Therefore, it may be more appropriate to consider specific guidelines for ‘drug-likeness’ dependent on the objectives of a project.