nanomaterials Article A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles Mahsa Mirzaei 1 , Irini Furxhi 1,2, *, Finbarr Murphy 1,2 and Martin Mullins 1   Citation: Mirzaei, M.; Furxhi, I.; Murphy, F.; Mullins, M. A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles. Nanomaterials 2021, 11, 1774. https://doi.org/10.3390/ nano11071774 Academic Editors: M. Teresa P. Amorim, Helena Prado Felgueiras, Joana C. Antunes, Elena Ivanova and Constantine D. Stalikas Received: 20 May 2021 Accepted: 6 July 2021 Published: 7 July 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland; mahsa.mirzaei@ul.ie (M.M.); finbarr.murphy@ul.ie (F.M.); martin.mullins@ul.ie (M.M.) 2 Transgero Limited, Cullinagh, Newcastle West, V42V384 Limerick, Ireland * Correspondence: irini.furxhi@ul.ie; Tel.: +353-85-106-9771 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 (R 2 = 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. Keywords: nanoparticles; antibacterial effect; antimicrobial capacity; biofilm; machine learning 1. Introduction Antibiotic resistance is increasing to alarmingly high levels, the resistance mechanisms threatening our ability to treat common infectious diseases which leads to a global health risk [1]. Antibacterial agents are compounds that can be classified as either bactericidal, completely inhibiting and eradicating bacteria, or bacteriostatic, which inhibits bacterial growth [2]. However, several factors may influence this classification, including growth conditions, bacterial density or test duration [3]. More importantly, the effectiveness of most compounds depends on the type of bacteria (Gram-positive and Gram-negative bacteria) exposed to [2,4]. The majority of existing antibacterial agents are chemically modified natural compounds, e.g., β-lactamines (i.e., penicillin), cephalosporins or carbapenems; or purely natural products (i.e., aminoglycosides), and purely synthetic antibiotics, such as sulfonamides [2,5]. As a result of the recurrence of infections, the microorganisms develop resistance due to inherent genetic changes [6,7]. With the excessive use or misuse of antibacterial agents, the emergence of resistance to antibacterial drugs has become one of the most significant public health challenges [8,9]. Nanomaterials 2021, 11, 1774. https://doi.org/10.3390/nano11071774 https://www.mdpi.com/journal/nanomaterials