ArticleFrontiers in chemistry2026
Artificial intelligence modeling and investigation of metal organic frameworks in drug delivery: modeling of loading capacity and toxicity behavior.
Article in Frontiers in chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Biomacromolecule-MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI-Assisted Screening.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Artificial intelligence-based modeling and validation for prediction of drug delivery capacity and cytotoxicity in design of porous materials.Frontiers in chemistry · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
In this work, our aim was to develop predictive models of Drug Loading Capacity (g/g) and Cell Viability (%) in MOFs (Metal Organic Frameworks) for evaluation of these materials in drug delivery applications and assess their performance. We employed Gaussian Process Regression (GPR) and its advanced variants: Sparse Gaussian Process Regression (SGPR) and Deep Gaussian Process Regression (DGPR) as the base of our modeling framework to estimate the target values. The dataset was carefully preprocessed, involving outlier detection using the z-score method and normalization with Min-Max scaling approach. Dimensionality reduction was executed using Principal Component Analysis (PCA), while hyperparameter optimization was performed with the Cheetah Optimizer (CO), a metaheuristic method. Among the models evaluated, DGPR demonstrated superior performance, achieving mean cross-validation
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