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Updated: Oct 26, 2025
10:37
Spatial Separation of Molecular Conformers and Clusters
Published on: January 9, 2014
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Ensemble completeness in conformer sampling: the case of small macrocycles.
Lea Seep1, Anne Bonin1, Katharina Meier1
1Pharmaceuticals R&D, Digital Technologies, Bayer AG, 42096, Wuppertal, Germany.
Journal of Cheminformatics
|July 30, 2021
Summary
Comparing conformer generation algorithms reveals that molecular dynamics (MD) simulations with explicit solvent provide distinct conformational maps, unlike implicit solvent models which yield similar results. Post-optimized MD ensembles cover more conformational space than generator ensembles.
Area of Science:
- Computational Chemistry
- Molecular Modeling
- Drug Discovery
Background:
- Conformer ensembles are crucial for computational chemistry applications.
- Accurate sampling of molecular conformations is essential for predicting molecular properties.
- Existing algorithms for conformer generation require thorough evaluation.
Purpose of the Study:
- To compare the performance of three conformer generation algorithms: Biovia BEST, Schrödinger Prime macrocycle sampling (PMM), and Conformator (CONF).
- To assess the completeness and diversity of conformer ensembles generated by these algorithms.
- To evaluate the impact of explicit versus implicit solvent models on conformational sampling.
Main Methods:
- Generated conformer ensembles using BEST, PMM, and CONF algorithms.
- Performed exhaustive molecular dynamics (MD) simulations with explicit solvent.
- Utilized conformation maps with principal component analysis (PCA) based on ring torsions.
- Applied cluster overlap, variance statistics, and Mahalanobis distance metrics to quantify ensemble space coverage.
Main Results:
- MD simulations with explicit solvent produced distinct conformational maps across different macrocycles, charge states, and solvents.
- Post-optimized conformers using implicit solvent models showed similar maps regardless of solvent.
- Post-optimized MD ensembles covered significantly larger conformational space than generator ensembles (PMM > BEST >> CONF).
- 3D polar surface area distributions were similar across most macrocycles, except for compound 7.
Conclusions:
- The choice of solvent model (explicit vs. implicit) critically impacts conformational sampling and map distinctiveness.
- Post-optimized MD ensembles offer superior conformational space coverage compared to generator algorithms.
- Algorithm performance ranking for ensemble generation is PMM > BEST >> CONF.
- 3D polar surface area is largely independent of charge state and solvent for macrocycles, with exceptions for strained systems.
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