A polynomial equation devised using machine learning to predict the antibacterial activity of silver nanoparticles

The antibacterial activity of silver nanoparticles has been well-researched throughout the years, and massive data has been generated describing a specific nanoparticle sample and its antibacterial activity by performing laboratory experiments; however, none have utilized this data to create a means of predicting the antibacterial activity. In this paper, we developed a polynomial equation using machine learning that predicts the antibacterial activity of silver nanoparticles against S. aureus and E. coli. Only studies featuring spherical silver nanoparticles without any surface modifications that may enhance the antibacterial activity were considered, and the studies must test the antibacterial activity in terms of the number of bacterial colonies left after treatment to calculate the efficiency. The equation takes the size and amount of the nanoparticles in a particular sample as inputs and predicts its antibacterial activity in terms of the percentage of bacterial colonies left after treatment. The equation was validated for its accuracy and was found to accurately predict the antibacterial activity based on the values of the relevant features. The study is the first of its kind and contributes to the field by reducing the effort and resource consumption in laboratories and providing a simple and efficient means of predicting antibacterial activity.