  In molecular discovery and drug design, structure-property relationships and
activity landscapes are often qualitatively or quantitatively analyzed to guide
the navigation of chemical space. The roughness (or smoothness) of these
molecular property landscapes is one of their most studied geometric
attributes, as it can characterize the presence of activity cliffs, with
rougher landscapes generally expected to pose tougher optimization challenges.
Here, we introduce a general, quantitative measure for describing the roughness
of molecular property landscapes. The proposed roughness index (ROGI) is
loosely inspired by the concept of fractal dimension and strongly correlates
with the out-of-sample error achieved by machine learning models on numerous
regression tasks.
