Background: With long and costly drug development times there is a need in the pharmaceutical industry to prioritize targets early in the drug discovery process. One of the possible criteria is 'protein drugability', a term with multiple understandings in the literature. Among others, it is the likelihood of finding a selective, low-molecular weight molecule that binds with high affinity to the target.
Objective: Which methods are available for drugability prediction? What can be achieved by such predictions and how can they influence the target prioritization process?
Methods: The main focus is on sequence- and structure-related computational methods for drugability prediction, giving an overview on their background as well as their bias and limitations with an emphasis on the structural biology point of view.
Results/conclusion: Structural drugability assessment presents one criterion for prioritization of a target portfolio by enabling classification of targets into low, average, or high drugability.