High-fidelity machine learning models for predicting antibacterial effects of cerium oxide nanoparticles across bacterial strains

Abstract The primary objective of this study is to develop and validate robust data-driven models for accurately predicting bacterial growth inhibition induced by cerium oxide nanoparticles across different bacterial strains and experimental conditions. This study aims to develop and validate data-driven predictive models to quantify bacterial growth inhibition induced by cerium oxide nanoparticles under diverse experimental conditions, with the goal of supporting antibacterial nanotechnology research. To this end, sophisticated AI methods, including Convolutional Neural Networks (CNN), Multi-layer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Ensemble Learning (EL), were employed to model bacterial cell concentration (OD600) with high precision. Model hyperparameters were optimized using the Coupled Simulated Annealing (CSA) technique to enhance predictive performance. A comprehensive dataset comprising 484 experimental observations was compiled, with 387 samples allocated for training and 97 for validation. The study considers two bacterial strains, Escherichia coli and Bacillus subtilis, cultivated in media containing cerium oxide nanoparticles with nominal sizes of 6 ± 3.5 nm, 15 ± 4.3 nm, 22 ± 5.7 nm, and 40 ± 10 nm (Samples A–D). Input features included bacterial type, nanoparticle size (medium type), nanoparticle concentration, and exposure time. Monte Carlo sensitivity analysis revealed that exposure time is the dominant factor governing bacterial cell concentration, followed by nanoparticle concentration, nanoparticle size, and bacterial strain. Among the evaluated models, MLP-ANN exhibited the highest predictive accuracy, achieving the greatest R2 values and the lowest RMSE and AARE%. Beyond predictive performance, the results provide insight into key drivers of nanoparticle-induced antibacterial activity and demonstrate how data-driven modeling can guide experimental prioritization. Overall, the proposed framework serves as a complementary tool to laboratory experiments, supporting more efficient investigation of antibacterial effects while preserving the necessity of experimental validation. These results demonstrate that AI-based models, particularly MLP-ANN, serve as a powerful complementary tool to laboratory experiments by enabling accurate prediction, guiding experimental prioritization, and reducing experimental burden while maintaining the necessity of experimental validation.