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Updated: Jun 19, 2026
10:37
Spatial Separation of Molecular Conformers and Clusters
Published on: January 9, 2014
Conformational analysis of macrocycles: finding what common search methods miss
Pascal Bonnet1, Dimitris K Agrafiotis, Fangqiang Zhu
1Johnson & Johnson Pharmaceutical Research and Development, Division of Janssen Pharmaceutica N.V., Turnhoutsweg 30, 2340 Beerse, Belgium. pbonnet@its.jnj.com
Journal of Chemical Information and Modeling
|October 8, 2009
Summary
For computational drug design, distance geometry methods like self-organizing superimposition (SOS) and stochastic proximity embedding (SPE) offer robust conformational sampling for macrocyclic molecules, outperforming other techniques.
Area of Science:
- Computational Chemistry
- Drug Design
- Molecular Modeling
Background:
- Computational drug design increasingly relies on virtual screening and 3D modeling.
- Exploring novel chemical structures necessitates advanced conformational sampling techniques.
- Macrocyclic molecules present unique challenges for conformational analysis.
Purpose of the Study:
- To compare the performance of popular conformational search algorithms.
- To evaluate methods on diverse macrocyclic molecule classes.
- To identify robust and efficient conformational sampling techniques.
Main Methods:
- Comparative analysis of multiple conformational search algorithms.
- Algorithms tested include Catalyst, CAESAR, MacroModel, MOE, Omega, Rubicon, SPE, and SOS.
- Focus on distance geometry and self-organizing algorithms.
Main Results:
- Distance geometry methods (SOS, SPE, Rubicon) demonstrated superior performance.
- MacroModel showed moderate success.
- Systematic search methods frequently failed to find low-energy conformations.
Conclusions:
- SOS and SPE are highly robust and universally applicable conformational search methods.
- SOS is preferred due to its exceptional speed.
- These findings are consistent with previous studies on drug-like molecules.
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