Blocking
the catalytic activity of urease has been shown to have
a key role in different diseases as well as in different agricultural
applications. A vast array of molecules have been tested against ureases
of different species, but the clinical translation of these compounds
has been limited due to challenges of potency, chemical and metabolic
stability as well as promiscuity against other proteins. The design
and development of new compounds greatly benefit from insights from
previously tested compounds; however, no large-scale studies surveying
the urease inhibitors’ chemical space exist that can provide
an overview of developed compounds to data. Therefore, given the increasing
interest in developing new compounds for this target, we carried out
a comprehensive analysis of the activity landscape published so far.
To do so, we assembled and curated a data set of compounds tested
against urease. To the best of our knowledge, this is the largest
data set of urease inhibitors to date, composed of 3200 compounds
of diverse structures. We characterized the data set in terms of chemical
space coverage, molecular scaffolds, distribution with respect to
physicochemical properties, as well as temporal trends of drug development.
Through these analyses, we highlighted different substructures and
functional groups responsible for distinct activity and inactivity
against ureases. Furthermore, activity cliffs were assessed, and the
chemical space of urease inhibitors was compared to DrugBank. Finally,
we extracted meaningful patterns associated with activity using a
decision tree algorithm. Overall, this study provides a critical overview
of urease inhibitor research carried out in the last few decades and
enabled finding underlying SAR patterns such as under-reported chemical
functional groups that contribute to the overall activity. With this
work, we propose different rules and practical implications that can
guide the design or selection of novel compounds to be screened as
well as lead optimization.