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CAESAR:  A New Conformer Generation Algorithm Based on Recursive Buildup and Local Rotational Symmetry Consideration

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Accelrys Inc., 10188 Telesis Court, San Diego, California 92121, and Computer Aided Molecular Design Group, Department of Pharmaceutical Chemistry, Institute of Pharmacy, University of Innsbruck, Innrain 52, A-6020 Innsbruck, Austria
Cite this: J. Chem. Inf. Model. 2007, 47, 5, 1923–1932
Publication Date (Web):August 11, 2007
https://doi.org/10.1021/ci700136x
Copyright © 2007 American Chemical Society

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    Abstract

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    A highly efficient conformer search algorithm based on a divide-and-conquer and recursive conformer build-up approach is presented in this paper. This approach is combined with consideration of local rotational symmetry so that conformer duplicates due to topological symmetry in the systematic search can be efficiently eliminated. This new algorithm, termed CAESAR (Conformer Algorithm based on Energy Screening and Recursive Buildup), has been implemented in Discovery Studio 1.7 as part of the Catalyst Component Collection. CAESAR has been validated by comparing the conformer models generated by the new method and Catalyst/FAST. CAESAR is consistently 5−20 times faster than Catalyst/FAST for all data sets investigated. The speedup is even more dramatic for molecules with high topological symmetry or for molecules that require a large number of conformers to be sampled. The quality of the conformer models generated by CAESAR has been validated by assessing the ability to reproduce the receptor-bound X-ray conformation of ligands extracted for the Protein Data Bank (PDB) and assessing the ability to adequately cover the pharmacophore space. It is shown that CAESAR is able to reproduce the receptor-bound conformation slightly better than the Catalyst/FAST method for a data set of 918 ligands retrieved from the PDB. In addition, it is shown that CEASAR covers the pharmacophore space as well or better than Catalyst/FAST.

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    *

     Corresponding author e-mail:  [email protected].

     Accelrys, Inc.

     University of Innsbruck.

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