Mini reviews in medicinal chemistry · 2026
Artificial Intelligence-enabled Antibiotic Discovery and Repurposing: Precision Strategies to Combat Antimicrobial Resistance.
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Pathak Manish, Dwivedi Akansha
Аннотация
INTRODUCTION: The effectiveness of some existing antibiotics is being compromised by the ever-rising rate at which antimicrobial resistance develops. Artificial Intelligence (AI) is one of the most significant technological innovations that have become possible through which chemical diversity can be explored, interactions between drugs and targets predicted, and novel compounds developed to address bacterial infections. This paper will discuss the role of artificial intelligence technology in antibiotic discovery.
METHODS: The capabilities of generative AI, deep learning, and machine learning to contribute to drug design, pharmacokinetic/pharmacodynamic property optimization, molecular screening, and prediction of mode of action were analyzed. Examples such as the discovery of halicin and peptide design using AI were also considered.
RESULTS: The advent of AI technology has made it possible to predict the activity and resistance mechanisms for antibiotics, as well as to create structurally diverse molecules. Multimodal data analysis, neural network docking, and reinforcement learning have helped in decreasing preclinical attrition.
DISCUSSION: Despite the huge promise offered by these technologies, several challenges must be overcome before these can be fully adopted. Issues such as low data quality, poor interpretability of the models, and regulatory requirements might slow down the implementation of AI technologies. Ethics and data formatting will be important determinants of the successful integration of AI technologies.
CONCLUSION: AI represents an efficient and promising tool for antibiotic development through the integration of advanced computational techniques with empirical evaluation.
METHODS: The capabilities of generative AI, deep learning, and machine learning to contribute to drug design, pharmacokinetic/pharmacodynamic property optimization, molecular screening, and prediction of mode of action were analyzed. Examples such as the discovery of halicin and peptide design using AI were also considered.
RESULTS: The advent of AI technology has made it possible to predict the activity and resistance mechanisms for antibiotics, as well as to create structurally diverse molecules. Multimodal data analysis, neural network docking, and reinforcement learning have helped in decreasing preclinical attrition.
DISCUSSION: Despite the huge promise offered by these technologies, several challenges must be overcome before these can be fully adopted. Issues such as low data quality, poor interpretability of the models, and regulatory requirements might slow down the implementation of AI technologies. Ethics and data formatting will be important determinants of the successful integration of AI technologies.
CONCLUSION: AI represents an efficient and promising tool for antibiotic development through the integration of advanced computational techniques with empirical evaluation.