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MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.
Camarillo-Cisneros, Javier; Ramirez-Alonso, Graciela; Arzate-Quintana, Carlos; Varela-Rodríguez, Hugo; Guzman-Pando, Abimael.
Afiliação
  • Camarillo-Cisneros J; Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomedicas, Universidad Autonoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.
  • Ramirez-Alonso G; Faculty of Engineering, Universidad Autonoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.
  • Arzate-Quintana C; Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomedicas, Universidad Autonoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.
  • Varela-Rodríguez H; Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomedicas, Universidad Autonoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico.
  • Guzman-Pando A; Computational Chemistry Physics Laboratory, Facultad de Medicina y Ciencias Biomedicas, Universidad Autonoma de Chihuahua, Campus II, 31125, Chihuahua, Mexico. aguzmanp@uach.mx.
Mol Divers ; 28(4): 1925-1945, 2024 Aug.
Article em En | MEDLINE | ID: mdl-39097550
ABSTRACT
Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https//github.com/AbimaelGP/MolGC/tree/main .
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software Idioma: En Revista: Mol Divers Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2024 Tipo de documento: Article País de afiliação: México País de publicação: Holanda
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software Idioma: En Revista: Mol Divers Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2024 Tipo de documento: Article País de afiliação: México País de publicação: Holanda