A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions

The
prediction of protein–ligand interactions and their
corresponding binding free energy is a challenging task in structure-based
drug design and related applications. Docking and scoring is broadly
used to propose the binding mode and underlying interactions as well
as to provide a measure for ligand affinity or differentiate between
active and inactive ligands. Various studies have revealed that most
docking software packages reliably predict the binding mode, although
scoring remains a challenge. Here, a diverse benchmark data set of
99 matched molecular pairs (3D-MMPs) with experimentally determined
X-ray structures and corresponding binding affinities is introduced.
This data set was used to study the predictive power of 13 commonly
used scoring functions to demonstrate the applicability of the 3D-MMP
data set as a valuable tool for benchmarking scoring functions.