ArticleScientific reports2025
Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 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
- Advances in Camptothecin-Class Compounds Nanomedicines: A Comprehensive Review of Antitumor Strategies.Pharmaceutics · 2026Review
- Artificial intelligence modeling and investigation of metal organic frameworks in drug delivery: modeling of loading capacity and toxicity behavior.Frontiers in chemistry · 2026Article
- 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal organic frameworks (MOFs) have indicated great capacity and applications in drug delivery owing to their porous structures. Analysis of their drug loading capacity as well as cytotoxicity was carried out in this study via machine learning. The study employs a stacking regression approach to predict two critical outputs: Cell Viability (%) and Drug Loading Capacity (g/g) in MOFs. The proposed framework combines base models, including Multilayer Perceptron (MLP), Random Forest (RF), and Quantile Regression (QR), with a meta-model for enhanced accuracy and robustness. Principal Component Analysis (PCA) was applied to reduce dimensionality, and the Water Cycle Algorithm was used to optimize hyperparameters. Evaluation metrics, including R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.