ArticleiScience2026
Predicting antibacterial activity of silver nanoparticles using physicochemical descriptors.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
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
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