Advanced hybrid intelligence evaluation of nanomedicine delivery to various organs using machine learning and adaptive tree structured Parzen estimator.

We have examined the efficiency of drug delivery for targeted therapy by theoretical models. Machine learning strategy was tested to analyze the drug delivery of nanomedicines to the desired sites for efficient treatment. The inputs to the models are properties of nanoparticles, tumor model, cancer type, administration dose of drug, while the outputs are delivery efficiency of drug in various organs. The ML models employed include Elastic Net Regression (EN), K-Nearest Neighbors (KNN), Polynomial Regression (PR), and an ensemble method using Boosting to leverage the strengths of these individual models. The dataset includes both categorical and numerical variables related to nanoparticle properties and their distribution across organs like the tumor, spleen, heart, liver, lung, and kidney. Hyperparameter optimization was enhanced using the Adaptive Tree-Structured Parzen Estimator (ATPE), which improves upon traditional Bayesian optimization methods. The results demonstrated that the AdaBoost KNN model consistently outperformed other models in predicting nanoparticle biodistribution, with high R² values and low RMSE across multiple organ targets, thus proving effective in capturing complex input-output relationships. This approach offers a robust framework for advancing predictive modeling in biomedical applications involving nanoparticles.