Much interest has been paid in the last decade on molecular predictors of promiscuity, including
molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations
between promiscuity and those descriptors seemingly being context-dependent. It has been observed
that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include
multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors
suggest that the topology of a given drug could be used to infer its therapeutic applications.
Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors
and other promiscuity predictors across different therapeutic categories. Working with the publicly
available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering
methods were applied to the descriptors mean values of the therapeutic categories after the refinement
of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly
available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning
examples that could be classified into the therapeutic categories considered by the aforementioned clusters
(111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3-
cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity
seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic
categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening
and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic
categories.