  Pairs of similar compounds that only differ by a small structural
modification but exhibit a large difference in their binding affinity for a
given target are known as activity cliffs (ACs). It has been hypothesised that
quantitative structure-activity relationship (QSAR) models struggle to predict
ACs and that ACs thus form a major source of prediction error. However, a study
to explore the AC-prediction power of modern QSAR methods and its relationship
to general QSAR-prediction performance is lacking. We systematically construct
nine distinct QSAR models by combining three molecular representation methods
(extended-connectivity fingerprints, physicochemical-descriptor vectors and
graph isomorphism networks) with three regression techniques (random forests,
k-nearest neighbours and multilayer perceptrons); we then use each resulting
model to classify pairs of similar compounds as ACs or non-ACs and to predict
the activities of individual molecules in three case studies: dopamine receptor
D2, factor Xa, and SARS-CoV-2 main protease. We observe low AC-sensitivity
amongst the tested models when the activities of both compounds are unknown,
but a substantial increase in AC-sensitivity when the actual activity of one of
the compounds is given. Graph isomorphism features are found to be competitive
with or superior to classical molecular representations for AC-classification
and can thus be employed as baseline AC-prediction models or simple
compound-optimisation tools. For general QSAR-prediction, however,
extended-connectivity fingerprints still consistently deliver the best
performance. Our results provide strong support for the hypothesis that indeed
QSAR methods frequently fail to predict ACs. We propose twin-network training
for deep learning models as a potential future pathway to increase
AC-sensitivity and thus overall QSAR performance.
