Knowledge-Based Conformer Generation Using the Cambridge Structural Database

Fast
generation of plausible molecular conformations is central
to molecular modeling. This paper presents an approach to conformer
generation that makes extensive use of the information available in
the Cambridge Structural Database. By using geometric distributions
derived from the Cambridge Structural Database, it is possible to
create biologically relevant conformations in the majority of cases
analyzed. The paper compares the performance of the approach with
previously published evaluations, and presents some cases where the
method fails. The method appears to show significantly improved performance
in reproduction of the conformations of structures observed in the
Cambridge Structural Database and the Protein Data Bank as compared
to other published methods of a similar speed.