Embracing chemical and structural diversity with UCONGA: A universal conformer generation and analysis program.
J Mol Graph Model
; 77: 286-294, 2017 10.
Article
en En
| MEDLINE
| ID: mdl-28915446
Molecular properties depend on molecular structure, so the first step in any computational chemistry investigation is to generate all thermally accessible conformers. Typically it is necessary to make a trade-off between the number of conformers to be explored and the accuracy of the method used to calculate their energies. Ab initio potential energy surface scans can, in principle, be applied to any molecule, but their conformational cost scales poorly with both molecular size and dimensionality of the search space. Specialized conformer generation techniques rely on parameterized force fields and may also use knowledge-based rules for generating conformers, and are typically only available for drug-like organic molecules. Neither approach is well-suited to generating or identifying chemically sensible conformers for larger non-organic molecules. The Universal CONformer Generation and Analysis (UCONGA) program package fills this niche. It requires no parameters other than built-in atomic van der Waals radii to generate comprehensive ensembles of sterically-allowed conformers, for molecules of arbitrary composition and connectivity. Analysis scripts are provided to identify representative structures from clusters of similar conformers, which may be further refined by subsequent geometry optimization. This approach is particularly useful for molecules not described by parameterized force fields, as it minimizes the number of computationally intensive ab initio calculations required to characterize the conformer ensemble. We anticipate that UCONGA will be particularly useful for computational and structural chemists studying flexible non-drug-like molecules.
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Estructura Molecular
/
Conformación Molecular
Idioma:
En
Revista:
J Mol Graph Model
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2017
Tipo del documento:
Article
País de afiliación:
Nueva Zelanda
Pais de publicación:
Estados Unidos