The impact activity cliffs have on drug discovery is double-edged. For
instance, whereas medicinal chemists can take advantage of regions in
chemical space rich in activity cliffs, QSAR practitioners need to escape
from such regions. The influence of activity cliffs in medicinal chemistry
applications is extensively documented. However, the ‘dark side’ of
activity cliffs (i.e. their detrimental effect on the development of
predictive machine learning algorithms) has been understudied. Similarly,
limited amounts of work have been devoted to propose potential solutions
to the drawbacks of activity cliffs in similarity-based approaches. In this
review, the duality of activity cliffs in medicinal chemistry and
computational approaches is addressed, with emphasis on the rationale
and potential solutions for handling the ‘ugly face’ of activity cliffs.