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Knowledge-Based Conformer Generation Using the Cambridge Structural Database
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DOI：10.1021/acs.jcim.7b00697.png)
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Abstract
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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.
Keywords:
CRYSTAL-STRUCTURES
VALIDATION
PREFERENCES
ALGORITHM
MOLECULES
SPACE
ANGLE
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