Mining
the steadily increasing amount of chemical and biological
data is a key challenge in drug discovery. Graph databases offer viable
alternatives for capturing interrelationships between molecules and
for generating novel insights for design. In a graph database, molecules
and their properties are mapped to nodes, while relationships are
described by edges. Here, we introduce a graph database for navigation
in chemical space, analogue searching, and structureâactivity
relationship (SAR) analysis. We illustrate this concept using hERG
channel inhibitors from ChEMBL to extract SAR knowledge. This graph
database is built using different relationships, namely 2D-fingerprint
similarity, matched molecular pairs, topomer distances, and structureâactivity
landscape indices (SALI). Typical applications include retrieving
analogues linked by single or multiple edge paths to the query compound
as well as detection of nonadditive SAR features. Finally, we identify
triplets of linked molecules for clustering. The speed of searching
and analysis allows the user to interactively navigate the database
and to address complex questions in real-time.