Integrating machine learning and physics-based modeling for predictive design of gemcitabine-loaded nanocomposites.

ABSTRACT: This study aims to develop a machine learning framework to predict loading efficiency and encapsulation efficiency in gemcitabine-loaded nanocomposites, thereby overcoming the limitations of purely experimental approaches. A curated dataset of 59 experimental formulations, augmented with 200 physics-informed synthetic data points, was used to train and compare multiple machine learning algorithms. A Physics-Informed Machine Learning algorithm incorporated interactions between drugs and polymers, as well as kinetic release. Model performance was evaluated using the coefficient of determination, root mean square error, and mean absolute error, along with SHapley Additive exPlanations values to assess the influence of individual variables. The XGBoost algorithm yielded the highest value of pr

SUBMITTER: Rahdar A 

PROVIDER: S-EPMC12905131 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature