هوش مصنوعی در کشف و توسعه دارو در برابر مقاومت ضد میکروبی
کد: G-1894
نویسندگان: Abed Zahedi Bialvaei * ℗
زمان بندی: زمان بندی نشده!
برچسب: دستیار مجازی هوشمند
دانلود: دانلود پوستر
خلاصه مقاله:
خلاصه مقاله
Background and Aims: Antimicrobial resistance (AMR) presents a formidable global health challenge, jeopardizing the efficacy of current antibiotics and posing a substantial threat to public health. The escalating prevalence of AMR demands innovative solutions. However, the traditional drug discovery process for combating AMR is marked by significant costs, prolonged timelines, frequent inefficiencies, and numerous developmental hurdles. This study explores the potential role of artificial intelligence (AI) in addressing AMR through drug discovery and development. Method: This review employed a systematic bibliometric approach to identify and analyze relevant literature on AI applications in AMR drug discovery. The primary database used was the Web of Science Core Collection (WoSCC), recognized for its comprehensive coverage of scientific publications and suitability for bibliometric analysis. The search was conducted on 2024, and covered publications from 2014 to 2024. Only original articles and reviews written in English were included. Results: Article Selection and Statistics including 2,408 publications (2014–2024) that included in the bibliometric analysis. Annual publication growth was observed, increasing from 4 articles in 2014 to 549 in 2023, indicating a rapidly expanding research field. The United States led with 707 publications, followed by China (581) and India (233). Top contributing institutions included the Chinese Academy of Sciences (53 articles), Harvard Medical School (43), and University of California San Diego (26). Citation analysis highlighted two major breakthroughs: AlphaFold’s protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword and cluster analysis revealed six enduring research themes: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking. Among 483 identified keywords, “machine learning,” “antibiotic resistance,” and “prediction” were most frequent. Thirty-three keywords appeared more than 50 times, reflecting the centrality of AI and data analysis in this domain. Conclusion: Research output in this field is rapidly increasing, with significant international and institutional collaboration. These findings underscore the growing impact and promise of AI in overcoming the challenges posed by AMR, while also highlighting areas for further research and interdisciplinary collaboration.
کلمات کلیدی
Artificial Intelligence, Point-of-care, Convolutional Neural Networks
