Calculating Similarities between Biological Activities in the MDL Drug Data Report Database

There are a number of licensed databases that assign biological activities to druglike compounds. The MDL
Drug Data Report (MDDR), compiled from the patent literature, is a popular example. It contains several
hundred distinct activities, some of which are therapeutic areas (e.g., Antihypertensive) and some of which
are related to specific enzymes or receptors (e.g., ACE inhibitor). There are several data mining applications
where it would be useful to calculate a similarity between any two activities. Two distinct activity labels
can have a significant similarity for a number of reasons:  two activities can be nearly synonymous (e.g.,
CCK B antagonist vs Gastrin antagonist), one activity may be a subset of another (e.g., Dopamine (D2)
agonist vs Dopamine agonist), or an activity can be the mechanism by which another activity works (e.g.,
ACE inhibitor vs Antihypertensive), etc. In an ideal world, similarities for two activities could be calculated
simply by comparing the compounds they have in common, but in hand-curated databases such as the
MDDR the assignment of activities to compounds are inevitably inconsistent and incomplete. We propose
a number of methods of calculating activity−activity similarities that hopefully compensate for errors in
hand-curation. Two of these, TIMI and trend vector, show promise. Soft clustering of the activities using
a union of similarity methods shows a reasonable association of therapeutic areas with their mechanisms.