Computer Methods for the Antimicrobial Efficacy of Metal Nanoparticles Against MDR Microbes - Shanza Anzar; Ayesha Sohail; Lubna Sherin; Peter S. Kim (Journal of Nanotechnology)
The current high mortality rate associated with infectious diseases is an alarming issue worldwide. The emergence of multidrug-resistant bacterial strains has led to the development of new ailments and the resurgence of old pathogenic infections. To address this issue, metal and metal oxide nanoparticles have emerged as potent nanoantimicrobial agents, offering an alternative to clinically used conventional antibiotics. This study employs support vector machine learning classification algorithms to identify the key attributes of inorganic nanoparticles that influence their antimicrobial efficacy. Key attributes such as type, size, and concentration of nanoparticles, along with transient analysis factors such as exposition time and bactericidal potency evaluation methods, are thoroughly analyzed and documented. The nonlinearity in this time-dependent data and the complexity of the potent size and concentration of these nano-antibiotics are simplified through a stepwise machine learning classification process involving encoding and decoding, supported by smart optimization of classifiers. These classifiers help identify the best attributes of nanoparticles to achieve the highest in vitro antimicrobial efficacy of unknown metallic nanoparticles in a robust and cost-efficient manner. The integration of machine learning techniques in nanomaterial research enhances the understanding and development of smart materials, and thus, the research in this domain can achieve noticeable milestones.