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.
Almomani et al. (Wed,) studied this question.
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