Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Area of Science:

• Pharmacology and Pharmaceutical Sciences
• Computer Science and Artificial Intelligence
• Bibliometrics and Scientometrics

Background:

• Traditional drug discovery relies on rule-based processes.
• Artificial intelligence (AI) is an emerging trend in drug discovery, offering increased efficiency and novel therapeutic development.
• A holistic overview of AI-based drug discovery research is currently lacking.

Purpose of the Study:

• To address the research gap by providing a comprehensive bibliometric analysis of AI in drug discovery.
• To identify and analyze the interrelationships between AI algorithms, research institutions, countries, and funding bodies in this field.
• To develop a research agenda for future studies in AI-driven drug discovery.

Main Methods:

• Bibliometric analysis of 3884 articles published between 1991 and 2022.
• Utilized qualitative and quantitative methods including performance analysis, science mapping, and thematic analysis.
• Examined the landscape of AI algorithms, institutional contributions, international collaborations, and funding sources.

Main Results:

• Identified key trends, influential research areas, and major contributors in AI-based drug discovery.
• Mapped the scientific landscape, revealing collaborations and knowledge dissemination patterns.
• Highlighted the growing impact and interdisciplinary nature of AI in pharmaceutical research.

Conclusions:

• AI is significantly transforming drug discovery, necessitating a structured understanding of its research landscape.
• The bibliometric analysis provides a foundational overview and identifies areas for future investigation.
• A proposed research agenda will guide future efforts in optimizing AI applications for novel drug development.