Predicting PLGA nanoparticle size and zeta potential in synthesis for application of drug delivery via machine learning analysis

This study employed multiple machine learning (ML) methods to model and predict key attributes of PLGA nanoparticles, specifically particle size and zeta potential. The predictions were based on input variables, including PLGA polymer type, PLGA concentration, anti-solvent type, and anti-solvent concentration. Advanced regression models, including Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), and Adaptive Neuro-Fuzzy Inference System (ANFIS), were applied to a dataset following rigorous preprocessing. This preprocessing involved Leave-One-Out encoding for categorical variables, Z-score-based outlier detection, and Min–Max normalization for numerical inputs. GPR outperformed the other models in predicting particle size and zeta potential, achieving the best test R2 scores of 0.9427 and 0.9841, respectively. Furthermore, GPR recorded the lowest total Mean Squared Error (MSE) for particle size (87.504 nm) and zeta potential (1.103 mV), with minimal Mean Absolute Percentage Errors (MAPE) of 3.76% and 2.31%, underscoring its precision and robustness. Cross-validation results further affirmed GPR’s consistency, with a mean 0.9611 for zeta potential and R2 of 0.9588 for particle size and low standard deviations (0.0141 and 0.0083, respectively).