The activity cliff (AC) concept is widely applied in medicinal chemistry. ACs are formed by compounds with small structural changes having large differences in potency. Accordingly, ACs are a primary source of structure-activity relationship (SAR) information. Through large-scale compound data mining it has been shown that the vast majority of ACs are formed in a coordinated manner by groups of structurally analogous compounds with significant potency variations. In network representations coordinated ACs form clusters of varying size but frequently recurrent topology. Recently, computational methods have been introduced to systematically organize AC clusters and extract SAR information from them. AC clusters are widely distributed over compound activity classes and represent a rich source of SAR information. These clusters can be visualized in AC networks and isolated. However, it is challenging to extract SAR information from such clusters and make this information available to the practice of medicinal chemistry. Therefore, it is essential to go beyond subjective case-by-case analysis and design computational approaches to systematically access SAR information associated with AC clusters.
Keywords: AC clusters; SAR information extraction; activity cliffs; compound data mining; networks; structure–activity relationships.