Abstract：
:As computational drug design becomes increasingly reliant on virtual screening and on high-throughput 3D modeling, the need for fast, robust, and reliable methods for sampling molecular conformations has become greater than ever. Furthermore, chemical novelty is at a premium, forcing medicinal chemists to explore more complex structural motifs and unusual topologies. This necessitates the use of conformational sampling techniques that work well in all cases. Here, we compare the performance of several popular conformational search algorithms on three broad classes of macrocyclic molecules. These methods include Catalyst, CAESAR, MacroModel, MOE, Omega, Rubicon and two newer self-organizing algorithms known as stochastic proximity embedding (SPE) and self-organizing superimposition (SOS) that have been developed at Johnson & Johnson. Our results show a compelling advantage for the three distance geometry methods (SOS, SPE, and Rubicon) followed to a lesser extent by MacroModel. The remaining techniques, particularly those based on systematic search, often failed to identify any of the lowest energy conformations and are unsuitable for this class of structures. Taken together with our previous study on drug-like molecules (Agrafiotis, D. K.; Gibbs, A.; Zhu, F.; Izrailev, S.; Martin, E. Conformational Sampling of Bioactive Molecules: A Comparative Study. J. Chem. Inf. Model., 2007, 47, 1067-1086), these results suggest that SPE and SOS are two of the most robust and universally applicable conformational search methods, with the latter being preferred because of its superior speed.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Bonnet P,Agrafiotis DK,Zhu F,Martin Edoi
10.1021/ci900238asubject
Has Abstractpub_date
2009-10-01 00:00:00pages
2242-59issue
10eissn
1549-9596issn
1549-960Xjournal_volume
49pub_type
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