Predictive optimization of curcumin nanocomposites using hybrid machine learning and physics informed modeling.

Area of Science:

• Computational chemistry and materials science
• Nanotechnology and drug delivery systems
• Artificial intelligence in pharmaceutical research

Background:

• Optimizing nanocomposite formulations for drug delivery is crucial for maximizing therapeutic efficacy.
• Current methods often involve extensive experimental screening, leading to high costs and time investment.
• Integrating computational models can accelerate the discovery and optimization of novel nanocarriers.

Purpose of the Study:

• To develop a hybrid computational model combining Machine Learning (ML) and Physics-Informed Neural Networks (PINNs).
• To predict and maximize curcumin nanocomposite performance, specifically Loading Efficiency (LE%) and Encapsulation Efficiency (EE%).
• To identify key formulation parameters for efficient nanocarrier design.

Main Methods:

• Utilized quantitative experimental design with 74 synthesized nanocomposite formulations.
• Employed Python (v3.11) with scikit-learn, TensorFlow, and SHAP for data pre-processing and model construction.
• Developed a hybrid ML-PINN model integrating ML regressors (Gradient Boosting Regressor) with DLVO theory and diffusion-transport constraints.

Main Results:

• The hybrid model achieved high predictive performance (LE%: R²=0.89, RMSE=6.24; EE%: R²=0.87, RMSE=7.15).
• Physics-informed constraints improved model generalization by 23%, demonstrating robustness.
• Identified optimal parameters (particle diameter 80-200 nm, zeta potential -30 to -50 mV) and key variables (polymer ratio, surfactant concentration).

Conclusions:

• The hybrid ML-PINN model offers a stable and interpretable platform for nanocarrier optimization.
• This approach can reduce experimental screening costs by 40-60%.
• The framework shows potential for optimizing other bioactive drugs and for scalable pharmaceutical manufacturing.