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JDSICS · Lens · Application of Artificial Intelligence in Pharmaceutical Science: A Scientometric Analysis

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Article · Volume 5, Issue 2 · pp. 154–165

Application of Artificial Intelligence in Pharmaceutical Science: A Scientometric Analysis

Shambulinga B Jali1, M. Chaman Sab2, KK Mueen Ahmed3, Mohammed Yunus3, A.N Praveen5, C Nagaraj4, Mohamed Riyaz6

  1. 1Naavu School, IB (International Baccalaureate) Candidate, Bangalore, INDIA.
  2. 2A.R.G. College of Arts and Commerce, Davanagere, INDIA.
  3. 3Manuscript Technomedia LLP, No. 22 (New No. 40), 3rd Cross, Vivekananda Nagar, Bengaluru, INDIA.
  4. 4Sri Manjunathaswamy First Grade College, Davanagere, INDIA.
  5. 5Davangere University, Shivagangotri, Davangere, INDIA.
  6. 6A.R.M. First Grade College, Davanagere, INDIA.

Abstract

Artificial Intelligence (AI) has revolutionised pharmaceutical sciences by enhancing discovery, formulation development, pharmaceutical analysis and health care applications. The rapid growth of AI-driven research necessitates a comprehensive assessment of publication trends, research impact and emerging themes in this domain. This study aims to examine the global research landscape of AI applications in pharmaceutical science through a scientometric analysis of publications indexed in the Scopus database. A total of 249 publications published between 2000 and 2026 were retrieved from the Scopus database using a structured search strategy. Bibliometric and scientometric indicators were employed to evaluate publication growth, citation performance, authorship patterns, collaborative network, leading institutions, funding agencies and research themes; data visualisation and network analysis were performed using VOSviewer and BiblioShiny. The findings revealed a significant increase in research output, with an annual growth rate of 11.995. The dataset comprised 1.161 authors and 185 publication sources. The United States emerged as the leading contributor with 81 publications and 4,285 citations, while institutions such as the School of Pharmacy and UCL School of Pharmacy demonstrated substantial research impact. The Co-authorship network included 150 authors organised into 12 clusters with a Total link Strength of 1,064, indicating strong collaborative activity. Computer Science (38.15%) and Pharmacology, Toxicology and Pharmaceutics (33.73%) were the dominant subject areas. Keyword analysis identified artificial intelligence, Machine Learning, Deep Learning, Drug Discovery, Cheminformatics and Bioinformatics as the principal research themes. The study demonstrates that AI has become a major catalyst for innovation in pharmaceutical science, driving interdisciplinarity collaboration and advancing drug valuable insights into the intellectual structure, research trends and future directions of AI-enabled pharmaceutical science.

Keywords: Artificial Intelligence; Bibliometrics; Drug Discovery; Machine Learning; Pharmaceutical Science; Scientometric Analysis

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