AI-Based Drug Discovery for Antibiotic-Resistant Bacteria

DOI: https://doi.org/10.63345/ijrmp.v12.i10.1

Shweta Kumari

Independent Researcher

Greater Noida, India

Abstract

The emergence of antibiotic-resistant bacteria represents a critical threat to global public health, necessitating innovative approaches to drug discovery. Artificial Intelligence (AI) has emerged as a transformative tool in identifying novel compounds and predicting their biological activity against resistant pathogens. This manuscript provides an overview of AI-based methodologies for drug discovery targeting antibiotic-resistant bacteria, reviewing literature up to 2021. We discuss key algorithms, data-driven strategies, and integrated pipelines that combine cheminformatics, molecular docking, and machine learning. A statistical analysis is presented to compare the efficacy of different AI models, followed by a detailed description of our methodology, results, and insights from recent case studies. Finally, the manuscript concludes with a discussion on the potential impact of AI in accelerating the drug discovery process, while outlining future directions for research that could further refine these approaches.

Keywords

AI-based drug discovery; antibiotic-resistant bacteria; machine learning; cheminformatics; molecular docking; statistical analysis

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