ArticleScientific reports2025
Predictive optimization of curcumin nanocomposites using hybrid machine learning and physics informed modeling.
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 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- The Role and Potential of Nanotechnology in Improving Solubility and Enhancing Bioavailability.Pharmaceutics · 2026Review
- A physics-informed machine learning framework for predicting and mitigating doxorubicin nanocarrier toxicity in normal cells.Scientific reports · 2026Article
- Integrating multi-omics and machine learning to explore the role of amino acid metabolism in intervertebral disk degeneration.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the current research was to develop a hybrid computational model that involves the integration of Machine Learning (ML) with Physics-Informed Neural Networks (PINNs) to predict and maximize curcumin nanocomposite performance based on Loading Efficiency (LE%) and Encapsulation Efficiency (EE%). Quantitative experimental design comprising 74 synthesized nanocomposite formulations, selected by power analysis (α = 0.05, power = 0.8) to allow for statistical reliability. Pre-processing of data and model construction was undertaken in Python (v3.11) with libraries such as scikit-learn, TensorFlow, and SHAP (as downloaded from the Python Software Foundation). Different ML regressors were tested, and the highest predictive power was manifested by the Gradient Boosting Regressor (GBR). A custom-defined PINN was constructed to integrate mechanistic understanding from the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and diffusion-transport constraints. The hybrid model was also elucidated by SHapley Additive exPlanations (SHAP) analysis to find controlling formulation parameters. The optimized integrated model demonstrated extremely good predictive performance (LE%: 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 OpenQuestion graph.