This study demonstrates the integration of AI and CRISPR in drug discovery for antimicrobial resistance, suggesting new therapeutic approaches.
This study explores the innovative intersection of artificial intelligence (AI), CRISPR technology, and microbiome insights in microbial drug discovery, with a focus on overcoming the challenges posed by antimicrobial resistance (AMR) and emerging infectious diseases. The global threat of AMR necessitates the development of novel approaches that transcend traditional drug discovery methods. AI-driven platforms, including machine learning and high-throughput screening, are transforming drug design by enabling rapid identification of potential therapeutic targets and optimizing drug repurposing efforts. CRISPR-based gene-editing technologies offer precise tools to combat resistance mechanisms at the genetic level, while microbiome-based therapies hold promise for restoring microbial balance and improving immune responses. Despite significant progress, several challenges remain, including the integration of these technologies, data quality concerns, and the clinical translation of innovative solutions. The future of microbial drug discovery lies in the synergy of these technologies, providing a pathway toward personalized, effective treatments and combating the growing threat of AMR. This study provides a comprehensive overview of the current landscape, identifies research gaps, and outlines potential directions for future advancements.
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Abdulrazaq et al. (2025) studied this question.
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