Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome.

Small molecules with multi-target activity, also termed promiscuous compounds, are increasingly considered for pharmaceutical applications. The use of promiscuous chemical entities represents a departure from the compound specificity paradigm, one of the pillars of modern drug discovery. The popularity of promiscuous compounds is due to the concept of polypharmacology; another more recent drug discovery paradigm. It refers to insights that the efficacy of drugs often depends on interactions with multiple targets. Views concerning the extent to which small molecules might form well-defined interactions with multiple targets often differ, but comprehensive experimental investigations of promiscuity are currently rare. On the other hand, large volumes of active compounds and experimental measurements are becoming available and enable data-driven analyses of compound selectivity versus promiscuity. In this perspective, we discuss computational methods and data structures designed for promiscuity analysis. In addition, findings from large-scale exploration of activity profiles of inhibitors covering the human kinome are summarized. Although many kinase inhibitors are expected to be promiscuous, they are frequently found to be selective, which provides opportunities for target-directed drug discovery (rather than polypharmacology). We also discuss that machine learning yields evidence for the existence of structure-promiscuity relationships.