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AI-assisted models to predict chemotherapy drugs modified with C60 fullerene derivatives.
Robles-Hernández, Jonathan-Siu-Loong; Medina, Dora Iliana; Aguirre-Hurtado, Katerin; Bosquez, Marlene; Salcedo, Roberto; Miralrio, Alan.
Afiliação
  • Robles-Hernández JS; Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico.
  • Medina DI; Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Monterrey 64849, Mexico.
  • Aguirre-Hurtado K; Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico.
  • Bosquez M; Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico.
  • Salcedo R; Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito exterior s/n, Ciudad Universitaria, Coyoacán, 04510, Ciudad de México, Mexico.
  • Miralrio A; Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico.
Beilstein J Nanotechnol ; 15: 1170-1188, 2024.
Article em En | MEDLINE | ID: mdl-39319207
ABSTRACT
Employing quantitative structure-activity relationship (QSAR)/ quantitative structure-property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug-fullerene complexes (i.e., drug-pristine C60 fullerene and drug-carboxyfullerene C60-COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson's hard-soft acid-base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug-fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Beilstein J Nanotechnol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: México País de publicação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Beilstein J Nanotechnol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: México País de publicação: Alemanha