Prediction of individual compounds forming activity cliffs using emerging chemical patterns.

Activity cliffs are formed by structurally similar or analogous compounds having large potency differences. In medicinal chemistry, pairs or groups of compounds forming activity cliffs are of interest for structure-activity relationship (SAR) analysis and compound optimization. Thus far, activity cliff assessment has mostly been descriptive, i.e., compound data sets and activity landscape representations have been searched for activity cliffs in the context of SAR analysis. Only recently, first attempts have also been made to depart from descriptive analysis and predict activity cliffs. This has been done by building computational models that distinguish compound pairs forming activity cliffs from non-cliff pairs. However, it is principally more challenging to predict single compounds that participate in activity cliffs. Here, we show that individual compounds having high or low potency can be accurately predicted to form activity cliffs on the basis of emerging chemical patterns.