The development of any antibiotic is a tedious and time-intensive process. Low success rates of most candidate drug molecules in combination with lesser return of investments to companies are major challenges in the field of antibacterial development. The advent of artificial intelligence (AI) has led to an acceleration in drug development with algorithms being developed to identify viable hit molecules. With the rapid advancements in this field, algorithms that are being created using machine learning (ML) and neural networks (NN), are being leveraged for larger in-silico exploration and identification of newer antibacterials. (Figure ) depicts a clear accelerated growth in journal publications related to the use of AI in antibacterial research in the last decade. However, the increase in the number of patent publications remains relatively low indicating nascency in this field and that most research is still in the academic stage yet to reach commercialization. (Figure ) represents a VOSviewer analysis 393 for various concepts in the field of artificial intelligence in antibacterial research. In the network visualization, items are represented by their label and by default also by a circle. The size of the label and the circle are directly correlated to the weight of the item. Distance between two items indicates the relatedness of the concepts, the closer the stronger they are related. VOSviewer, by default, also assigns the nodes in a network to clusters (each indicated by a different color). A cluster is a set of closely related nodes. Each node in a network is assigned to exactly one cluster. It shows that in the last decade, use of AI in antibacterial research is being carried out to a larger extent for bacteria such as E. coli, S. aureus, M. tuberculosis, P. aeruginosa, etc. AI-related concept terms such as 'machine learning', 'simulation and modeling', 'algorithm' form more and intense connections with various bacteria indicating the increased interest and applicability of AI in this field (Figure ).