High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators

We
developed a cheminformatics pipeline for the fully automated
selection and extraction of high-quality protein-bound ligand conformations
from X-ray structural data. The pipeline evaluates the validity and
accuracy of the 3D structures of small molecules according to multiple
criteria, including their fit to the electron density and their physicochemical
and structural properties. Using this approach, we compiled two high-quality
datasets from the Protein Data Bank (PDB): a comprehensive dataset
and a diversified subset of 4626 and 2912 structures, respectively.
The datasets were applied to benchmarking seven freely available conformer
ensemble generators: Balloon (two different algorithms), the RDKit
standard conformer ensemble generator, the Experimental-Torsion basic
Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK.
Substantial differences in the performance of the individual algorithms
were observed, with RDKit and ETKDG generally achieving a favorable
balance of accuracy, ensemble size and runtime. The Platinum datasets
are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset.