CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys

A major goal in drug design is the
improvement of computational
methods for docking and scoring. The Community Structure Activity
Resource (CSAR) has collected several data sets from industry and
added in-house data sets that may be used for this purpose (www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline,
and Vertex and is working on obtaining data from several others. Combined
with our in-house projects, we are providing a data set consisting
of 6 protein targets, 647 compounds with biological affinities, and
82 crystal structures. Multiple congeneric series are available for
several targets with a few representative crystal structures of each
of the series. These series generally contain a few inactive compounds,
usually not available in the literature, to provide an upper bound
to the affinity range. The affinity ranges are typically 3–4
orders of magnitude per series. For our in-house projects, we have
had compounds synthesized for biological testing. Affinities were
measured by Thermofluor, Octet RED, and isothermal titration calorimetry
for the most soluble. This allows the direct comparison of the biological
affinities for those compounds, providing a measure of the variance
in the experimental affinity. It appears that there can be considerable
variance in the absolute value of the affinity, making the prediction
of the absolute value ill-defined. However, the relative rankings
within the methods are much better, and this fits with the observation
that predicting relative ranking is a more tractable problem computationally.
For those in-house compounds, we also have measured the following
physical properties: logD, logP, thermodynamic solubility, and pKa. This data set also provides a substantial
decoy set for each target consisting of diverse conformations covering
the entire active site for all of the 58 CSAR-quality crystal structures.
The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial,
publically available, curated data sets for use in parametrizing and
validating docking and scoring methods.