AI IN DRUG DISCOVERY: SURVEY ON HOW AI CAN BE USED TO FASTEN DRUG DISCOVERY. A BIBLIOMETRIC ANALYSIS (Journal of Medical & Health Sciences Review, 2025)
Background: Artificial intelligence (AI) in drug discovery is emerging as a transformative force, promising to revolutionize the development of new therapeutic agents. The increasing integration of AI techniques highlights its potential to optimize the discovery and development phases of drug development.Objective: To map the research domain of AI applications in drug discovery using a bibliometric analysis based on data from the Web of Science Core Collection.Methods• Study Period: Publications from January 1, 2010, to June 30, 2024.• Inclusion Criteria: Articles and reviews in English.• Data Set: A total of 1,234 publications, comprising 845 research articles and 389 reviews.• Analysis: Research activity trends, geographical distribution of publications, eminent researchers, institutional contributions, journal prominence, and keyword analysis.Results• Research Trends: A rising trend was observed, peaking at 175 publications in 2023.• Geographical Distribution:• The United States led with 420 publications and 18,540 citations.• Europe and Asia showed significant activity, particularly in China and India.• Eminent Researchers and Institutions:• Dr Emily Johnson, MIT, USA; Dr Robert Lee, UCSF, USA; Dr Ananya Patel, NIPER, India.• MIT led in publication count, while UCSF had the highest citation index.• Prominent Journals:• Journal of Medicinal Chemistry, Nature Reviews Drug Discovery, Bioinformatics.• Key Topics and Methods:• Keywords: Machine learning, neural networks, drug targeting, high-throughput screening.• Techniques: Deep learning algorithms and predictive modelling were highlighted as critical for drug discovery optimization.Conclusion: AI applications in drug discovery are on a significant upward trajectory, with contributions from leading institutions, researchers, and journals. The findings underscore the importance of international cooperation and interdisciplinary research in advancing AI-driven drug discovery. Such efforts are essential to enhance therapeutic agent development and improve treatment outcomes globally.