Model development using hybrid method for prediction of drug release from biomaterial matrix

Alqarni, Mohammed; Al Harthi, Shaimaa Mohammed; Alzubaidi, Mohammed Abdullah; Alqarni, Ali Abdullah; Shukr, Bandar Saud; Shawli, Hassan Talat

doi:10.1016/j.chemolab.2024.105216

Chemometrics and Intelligent Laboratory Systems

2024

DOI: 10.1016/j.chemolab.2024.105216

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Model development using hybrid method for prediction of drug release from biomaterial matrix

Mohammed Alqarni,

Shaimaa Mohammed Al Harthi,

Mohammed Abdullah Alzubaidi

et al.

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2024

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Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling

Alshahrani,

Alotaibi,

Begum

2024

Sci Rep

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Controlled release of a desired drug from porous polymeric biomaterials was analyzed via computational method. The method is based on simulation of mass transfer and utilization of artificial intelligence (AI). This study explores the efficacy of three regression models, i.e., Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Gradient Boosting (GB) in determining the concentration of a chemical substance ( C ) based on coordinates ( r , z ). Leveraging Firefly Optimization (FFA) for hyperparameter optimization, the models are fine-tuned to maximize their predictive performance. The findings unveil notable disparities in the performance metrics of the models, with GB showcasing the most impressive R 2 score of 0.9977, indicative of a remarkable alignment with the data. GPR closely trails with an R 2 score of 0.88754, while KRR falls short with an R 2 score of 0.76134. Additionally, GB manifests the most modest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) among the trio of models, further cementing its supremacy in predictive precision. These outcomes accentuate the significance of judiciously selecting regression methodologies and optimization approaches for adeptly modeling intricate spatial datasets.

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Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling

Alshahrani,

Alotaibi,

Begum

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Model development using hybrid method for prediction of drug release from biomaterial matrix

Cited by 1 publication

References 25 publications

Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling

Computational analysis of controlled drug release from porous polymeric carrier with the aid of Mass transfer and Artificial Intelligence modeling

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