Benchmarking Commercial Conformer Ensemble Generators.

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Accurate generation of molecular conformations is crucial for drug discovery and molecular modeling.
• Existing studies have evaluated free algorithms, but a comprehensive comparison of commercial tools is needed.

Purpose of the Study:

• To assess and compare the performance of eight commercial conformer ensemble generators against a leading free algorithm.
• To evaluate these algorithms using a high-quality benchmarking dataset of protein-bound ligand conformations.

Main Methods:

• Performance evaluation of nine conformer ensemble generators: eight commercial (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, OMEGA) and one free (RDKit distance geometry algorithm).
• Utilized a new version of the Platinum Diverse Dataset, comprising 2859 protein-bound ligand conformations from the PDB.
• Measured performance based on root-mean-square deviation (RMSD) between generated ensembles and reference protein-bound conformations.

Main Results:

• Commercial algorithms demonstrated high robustness, with over 99% successful processing and minimal geometrical errors.
• Median minimum RMSD values for commercial algorithms ranged from 0.46 to 0.61 Å for ensembles up to 250 conformers.
• The RDKit distance geometry algorithm (with minimization) showed performance comparable to mid-ranked commercial algorithms, presenting a viable free option.

Conclusions:

• Commercial conformer ensemble generators offer reliable and robust performance for molecular modeling applications.
• The RDKit distance geometry algorithm is a strong free alternative for generating molecular conformations.
• Recommendations are provided on algorithm selection and parametrization for optimal performance in various application scenarios.