Citation :

Rajesh Munirathnam, 2023. "Data-Driven Strategies for Combatting Antimicrobial Resistance: The Role of AI in Developing New Antibiotics" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 2: 112-125.

Abstract :

This paper aims to establish the fact that AMR is one of the biggest challenges that public health is facing in the 21st century. The emergence of new resistant bacterial strains has dampened the effectiveness of the available antibiotic drugs, hence making it necessary to look for new approaches that may help in the development of new antibiotics. Due to the complexity of the problem, new and well-developed means of machine learning based on artificial intelligence and data analysis have appeared. Machine learning and deep learning are the two AI tools that are rapidly being applied to antibiotic discovery to identify new compounds, predict bacterial resistance, design new drugs and the clinical trial process. The current paper aims to review how different AI-driven models are helpful in the search for new antibiotics, thus making a contribution to the fight against AMR. It is devoted to the description of AI application to different stages of antibiotic design, such as screening, mechanism prediction, and lead optimization, alongside examples of the use of AI in the creation of antibiotics. Finally, this paper looks into the shortcomings, difficulties, and ethical issues of incorporating AI into drug development. The study indicates that despite the encouraging opportunities of AI in antibiotic discovery, its benefits in that regard shall remain unfulfilled until models are improved, data is made available for further analysis, and there is more interdisciplinary research.

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Keywords :

Antimicrobial Resistance (AMR), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Antibiotic Discovery, Drug Development, Lead Optimization, Bacterial Resistance.