Predicting antibacterial activity of silver nanoparticles using physicochemical descriptors
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
Summary Quantitative prediction of antibacterial activity in green-synthesized silver nanoparticles (AgNPs) is critical for developing effective and sustainable antimicrobial agents, particularly against multidrug-resistant bacteria. However, variability in synthesis protocols, characterization methods, and biological assays, along with reliance on low-throughput microscopy, limits the ability to systematically link nanoparticle properties to antibacterial performance. Here, we present a materials-informatics framework that predicts inhibition zone diameter using machine-learning models trained on literature-derived datasets. Two complementary descriptor sets were evaluated: structural features from electron microscopy and optical parameters from UV-vis spectroscopy. Ensemble learning models achieved high predictive accuracy, with XGBoost reaching R> 0.94 for microscopy-based descriptors and CatBoost achieving R≈ 0.93 for optical features. Feature analysis identified nanoparticle size and bacterial concentration as dominant predictors. These findings demonstrate that UV-vis-derived descriptors can provide predictive capability comparable to microscopy-based features, offering a faster, scalable, and sustainable approach for designing and screening antibacterial nanomaterials. abs0010 Graphical abstract http://www.w3.org/1999/xlink float portrait ga1.webp undfig1 anchor portrait graphical abs0015 Highlights • Machine learning predicts AgNP antibacterial activity with high accu
Abstract
Summary Quantitative prediction of antibacterial activity in green-synthesized silver nanoparticles (AgNPs) is critical for developing effective and sustainable antimicrobial agents, particularly against multidrug-resistant bacteria. However, variability in synthesis protocols, characterization methods, and biological assays, along with reliance on low-throughput microscopy, limits the ability to systematically link nanoparticle properties to antibacterial performance. Here, we present a materials-informatics framework that predicts inhibition zone diameter using machine-learning models trained on literature-derived datasets. Two complementary descriptor sets were evaluated: structural features from electron microscopy and optical parameters from UV-vis spectroscopy. Ensemble learning models achieved high predictive accuracy, with XGBoost reaching R> 0.94 for microscopy-based descriptors and CatBoost achieving R≈ 0.93 for optical features. Feature analysis identified nanoparticle size and bacterial concentration as dominant predictors. These findings demonstrate that UV-vis-derived descriptors can provide predictive capability comparable to microscopy-based features, offering a faster, scalable, and sustainable approach for designing and screening antibacterial nanomaterials. abs0010 Graphical abstract http://www.w3.org/1999/xlink float portrait ga1.webp undfig1 anchor portrait graphical abs0015 Highlights • Machine learning predicts AgNP antibacterial activity with high accuracy u0010 • UV-vis descriptors rival microscopy features for activity prediction u0015 • Nanoparticle size and bacterial density are key activity determinants u0020 • GUI enables rapid screening of antibacterial silver nanoparticles u0025 simple ulist0010 author-highlights abs0020 Nanoparticles; Machine learning; Biomaterials teaser abs0025