Abstract

There is growing evidence demonstrating the use of computer modeling in biomedicine as new technologies emerge in every discipline. Computational, translational, experimental, and clinical methods are all combined in current drug development to find possible novel potential medications. However, limited attention has been given to summarizing global publication trends in this field. The aim of the current study is to describe and assess global trends in applying the bibliometric approach to drug development and discovery regarding the importance of molecular modeling. A literature search was conducted to extract all relevant papers on molecular modeling in drug discovery and development using the Scopus database. The data were gathered for the year 2005–2024. Insights are classified based on authors, title of publication sources, countries, type of documents, research domains, and so on. A total of 3,489 papers were retrieved, demonstrating a surge of interest in molecular modeling within drug discovery and development, with a significant rise in recent years. The top countries contributing were the United States, India, and China. Journal articles constituted the highest percentage of papers, and the most productive source was the Journal of Chemical Information and Modeling. The study highlights the tools and software used in modern drug development and discovery-related analysis that were developed using machine learning techniques. There has been an upsurge of interest in molecular modeling in drug discovery and development, as shown by a growing trend in research publications in recent years. This study is the first extensive bibliometric evaluation between 2005 and 2024 with specific emphasis on molecular modeling in drug development and discovery. The results serve as a useful guide for academicians, researchers, and policy-makers to identify global trends and future research directions in this new field.

Key words: Molecular Modelling, Drug, Discovery, Development, Bibliometric