A supervised machine learning tool to predict the bactericidal efficiency of nanostructured surface

Yaxi Chen; Hongyi Chen; Anthony Harker; Yuanchang Liu; Jie Huang

doi:10.1186/s12951-024-02974-8

A supervised machine learning tool to predict the bactericidal efficiency of nanostructured surface

J Nanobiotechnology. 2024 Dec 3;22(1):748. doi: 10.1186/s12951-024-02974-8.

Authors

Yaxi Chen¹, Hongyi Chen², Anthony Harker³, Yuanchang Liu¹, Jie Huang⁴

Affiliations

¹ Department of Mechanical Engineering, University College London, London, UK.
² Department of Computer Science, University College London, London, UK.
³ Department of Physics & Astronomy, University College London, London, UK.
⁴ Department of Mechanical Engineering, University College London, London, UK. jie.huang@ucl.ac.uk.

Abstract

The emergence and rapid spread of multidrug-resistant bacterial strains is a growing concern of public health. Inspired by the natural bactericidal surfaces of lotus leaves and shark skin, increasing attention has been focused on the use of mechano-bactericidal methods to create surfaces with antibacterial and/or bactericidal effects. There have been several studies exploring the bactericidal effect of nanostructured surfaces under various combinations of parameters. However, the correlation and synergies between these factors still need to be clarified. Recently machine learning (ML), which enables prediction or decision-making based on data, has been used in the field of biomaterials with promising results. In this study, we explored ML in nanotechnology to investigate the antimicrobial potential of nanostructured surfaces. A dataset of nanostructured surfaces and their antimicrobial properties was built by extracting the published literature. Based on the literature review and the distribution of our dataset, 70% bactericidal efficiency was selected as a practical benchmark for our classification model that balances stringent bactericidal performance with achievable targets in diverse conditions. Subsequently, we developed an ML classification model, which demonstrated an 81% accuracy in its predictive capability. A regression model was further developed to predict the value of bactericidal efficiency for nanostructured surfaces. Feature importance analysis of the ML models suggested that nanotopographical features have a greater influence on bactericidal properties than material properties, thus providing insight into the principles of the mechano-bactericidal effect of nanostructured surfaces. Overall, this ML model tool could help researchers to effectively select and design the parameters of the surface structure prior to experimentation, thereby improving the timeliness and reducing the number of experiments and the associated costs.

Keywords: Antimicrobial properties; Machine learning; Mechano-bactericidal activity; Nanotopography.

MeSH terms

Anti-Bacterial Agents* / chemistry
Anti-Bacterial Agents* / pharmacology
Bacteria / drug effects
Nanostructures* / chemistry
Nanotechnology / methods
Supervised Machine Learning*
Surface Properties*

Substances

Anti-Bacterial Agents