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A machine learning tool to predict the antibacterial capacity of nanoparticles

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Show simple item record Mirzaei, Mahsa Furxhi, Irini Murphy, Finbarr Mullins, Martin 2021-07-16T08:22:15Z 2021-07-16T08:22:15Z 2021
dc.description peer-reviewed en_US
dc.description.abstract The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nanoinformatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model’s validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physico-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools. en_US
dc.language.iso eng en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries Nanomaterials;11, 1774
dc.subject nanoparticles en_US
dc.subject antibacterial effect en_US
dc.subject antimicrobial capacity en_US
dc.subject biofilm en_US
dc.title A machine learning tool to predict the antibacterial capacity of nanoparticles en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.3390/nano11071774
dc.contributor.sponsor Horizon 2020 en_US
dc.contributor.sponsor European Union (EU)
dc.contributor.sponsor ERC
dc.relation.projectid 862444 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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