Your browser doesn't support javascript.
loading
Prediction of blood-brain barrier permeability using machine learning approaches based on various molecular representation.
Liang, Li; Liu, Zhiwen; Yang, Xinyi; Zhang, Yanmin; Liu, Haichun; Chen, Yadong.
Afiliación
  • Liang L; Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
  • Liu Z; Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
  • Yang X; Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
  • Zhang Y; Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
  • Liu H; Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
  • Chen Y; Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
Mol Inform ; 43(9): e202300327, 2024 Sep.
Article en En | MEDLINE | ID: mdl-38864837
ABSTRACT
The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor-intensive, cost-ineffective, and time-consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Permeabilidad / Barrera Hematoencefálica / Aprendizaje Automático Límite: Humans Idioma: En Revista: Mol Inform Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Permeabilidad / Barrera Hematoencefálica / Aprendizaje Automático Límite: Humans Idioma: En Revista: Mol Inform Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania