Benchmarking Commercial Conformer Ensemble Generators

We assess and compare the performance
of eight commercial conformer
ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import,
and OMEGA) and one leading free algorithm, the distance geometry algorithm
implemented in RDKit. The comparative study is based on a new version
of the Platinum Diverse Dataset, a high-quality benchmarking dataset
of 2859 protein-bound ligand conformations extracted from the PDB.
Differences in the performance of commercial algorithms are much smaller
than those observed for free algorithms in our previous study (J. Chem. Inf. Model. 2017, 57, 529–539). For commercial algorithms, the median minimum
root-mean-square deviations measured between protein-bound ligand
conformations and ensembles of a maximum of 250 conformers are between
0.46 and 0.61 Å. Commercial conformer ensemble generators are
characterized by their high robustness, with at least 99% of all input
molecules successfully processed and few or even no substantial geometrical
errors detectable in their output conformations. The RDKit distance
geometry algorithm (with minimization enabled) appears to be a good
free alternative since its performance is comparable to that of the
midranked commercial algorithms. Based on a statistical analysis,
we elaborate on which algorithms to use and how to parametrize them
for best performance in different application scenarios.