@article{100192,
  keywords = {Drug repurposing, Artificial Intelligence in Public Health, Virtual screening, Target identification, Emerging infectious diseases, Structure-based drug design},
  author = {Singh A},
  title = {Artificial intelligence for drug repurposing against infectious diseases},
  abstract = {<p>Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.</p>
},
  year = {2024},
  journal = {Artificial Intelligence Chemistry},
  volume = {2},
  pages = {1-14},
  publisher = {Elsevier BV},
  issn = {2949-7477},
  url = {https://www.sciencedirect.com/science/article/pii/S2949747724000290/pdfft?md5=061eb4d8766a2284afb22976a6d28b51&pid=1-s2.0-S2949747724000290-main.pdf},
  doi = {10.1016/j.aichem.2024.100071},
  language = {ENG},
}