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Artificial intelligence in antimicrobial drug discovery: predictive and generative strategies
Ximeng Duan1, Na Liu2, Lin Liu3
1Infection and Microbiology Research Laboratory for Women and Children, Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University, Jinan, P.R. China.
Artificial intelligence (AI) accelerates antimicrobial drug discovery, overcoming limitations of traditional methods. AI integration promises an "autonomous discovery" era for new antibacterial, antifungal, and antiviral drugs.
Area of Science:
- Drug Discovery
- Computational Biology
- Infectious Diseases
Background:
- Antimicrobial resistance is a global health crisis, outpacing drug development.
- Traditional antimicrobial discovery methods are inefficient, costly, and slow.
- Artificial intelligence (AI) offers new potential for rapid and precise drug discovery.
Purpose of the Study:
- To review recent AI-driven strategies for antimicrobial drug discovery.
- To cover small molecules, peptides, phages, and protein drugs against bacterial, fungal, and viral infections.
- To highlight advances and challenges in AI-based antimicrobial drug development.
Main Methods:
- Literature review of studies published between 2020-2025.
- Structured, iterative search of major scientific databases (PubMed, Web of Science, Scopus).
- Focus on AI applications including machine learning, deep learning, and natural language processing.
Main Results:
- AI is transforming antimicrobial drug discovery with increased speed and precision.
- AI strategies are being applied to diverse drug classes and infection types.
- Significant progress has been made in AI-driven predictive and generative approaches.
Conclusions:
- AI integration is key to the future of antimicrobial drug development, enabling 'autonomous discovery'.
- Challenges remain, including data bias, standardization, and clinical translation.
- AI holds promise for addressing the global threat of microbial infections.
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