DOI: https://doi.org/10.63345/10.63345/ijrmp.v13.i1.2
Meenal Rathore
Independent Researcher
Rajasthan, India
Abstract
Antibiotic resistance is an escalating global crisis, challenging conventional treatment regimens and demanding innovative therapeutic strategies. Recent advancements in artificial intelligence (AI) have paved the way for novel approaches in drug discovery, notably in the design of peptide drugs that can target resistant bacterial strains. This study investigates the impact of AI-generated peptide drugs on treating infections caused by antibiotic-resistant bacteria. Utilizing machine learning models to analyze vast chemical and biological datasets, AI-driven algorithms have been employed to generate and optimize peptide sequences with enhanced antimicrobial properties. These peptides are designed to exhibit strong activity against multidrug-resistant pathogens while minimizing cytotoxicity. Our research outlines the workflow from data acquisition and model training to in vitro validation and preclinical evaluation. Preliminary results indicate that AI-generated peptides not only show promising inhibitory activity against resistant bacteria but also possess unique structural features that may disrupt bacterial membranes or interfere with essential cellular functions. This manuscript reviews literature up to 2021, describes the methodology for generating and testing these peptides, presents experimental results, and discusses the potential clinical implications and future research directions. The integration of AI in peptide drug design represents a paradigm shift that could lead to more effective and targeted treatments for resistant bacterial infections, thereby reducing the morbidity and mortality associated with these pathogens.
Keywords
AI-generated peptides; antibiotic resistance; drug discovery; antimicrobial peptides; machine learning; resistant bacteria; peptide optimization
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