Mapping the Landscape of AI and Machine Learning in Drug Formulation Design: A Bibliometric Analysis

This bibliometric analysis explores AI and Machine Learning (ML) in drug formulation, particularly oral drug delivery. AI/ML addresses significant challenges in oral drug development, like solubility and bioavailability, enhancing efficiency in drug discovery. The study aimed to comprehensively overview the field by analyzing publication trends, identifying leading countries, institutions, and authors, and exploring co-citation networks to guide future research. Data was systematically collected from the Scopus database, focusing on English-language journal articles from 2023 to July 2025 concerning AI/ML in drug formulation. VOSviewer and other tools were used for data refinement and analysis, including publication trends, geographic contributions, and keyword co-occurrence. Results show robust publication growth, especially in 2024, indicating accelerating interest. The United States leads in research output, followed by China, India, and the UK, highlighting global collaboration. Influential publications underscore AI's practical applications in optimizing drug formulations. In conclusion, AI and ML are significantly advancing drug formulation, marked by increasing global engagement. Future research will likely focus on refining predictive models, optimizing manufacturing, and developing innovative AI-driven strategies for improved drug characteristics. The field continues to evolve through the synergy of technology and pharmaceutical sciences.