Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics

Abstract This work introduces a hybrid computational approach in which degree-based topological descriptors are harnessed with the aid of advanced regression models and artificial neural networks (ANNs) to predict the crucial physicochemical properties of 17 drugs for the treatment of bladder cancer...

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Autors principals:	Huiling Qin, Atef F. Hashem, Muhammad Farhan Hanif, Osman Abubakar Fiidow
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2025-07-01
Col·lecció:	Scientific Reports
Matèries:	Artificial Neural Networks (ANN) Topological Indices Degree-Based Descriptors Cubic Regression Linear Regression QSPR
Accés en línia:	https://doi.org/10.1038/s41598-025-14175-w
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