Benchmarking Commercial Conformer Ensemble Generators

By Nils-Ole Friedrich, C. Kops, Florian Flachsenberg, Kai Sommer, Matthias Rarey, Johannes Kirchmair. 2017

106 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles (2021, methodology): “OMEGA [Hawkins et al., 2010, Hawkins and Nicholls, 2012, Friedrich et al., 2017], a rule-based method, is one of the most established commercial software, with more than a decade of continuous development.”
Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery (2022, unknown): “…alignment (Labute et al., 2001; Jain Ajay, 2004; Chan, 2017; Brown et al., 2019), conformer generation (Cappel et al., 2015; Kothiwale et al., 2015; Friedrich et al., 2017a, 2019), pharmacophore modeling (Hecker et al., 2002; Acharya et al., 2011; Vlachakis et al., 2015), structure-based (SB)…”
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, background): “Low-energy three dimensional conformer structures can be generated by a number of tools [118] and even better with quantum chemical methods [119].”