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Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.
Odugbemi, Adeshina I; Nyirenda, Clement; Christoffels, Alan; Egieyeh, Samuel A.
Afiliación
  • Odugbemi AI; South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town 7535, South Africa.
  • Nyirenda C; School of Pharmacy, University of the Western Cape, Bellville, Cape Town 7535, South Africa.
  • Christoffels A; National Institute for Theoretical and Computational Sciences (NITheCS), South Africa.
  • Egieyeh SA; Department of Computer Science, University of the Western Cape, Cape Town 7535, South Africa.
Comput Struct Biotechnol J ; 23: 2964-2977, 2024 Dec.
Article en En | MEDLINE | ID: mdl-39148608
ABSTRACT
Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Año: 2024 Tipo del documento: Article País de afiliación: Sudáfrica Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Comput Struct Biotechnol J Año: 2024 Tipo del documento: Article País de afiliación: Sudáfrica Pais de publicación: Países Bajos