Research Hotspots and Trends of Artificial Intelligence in Drug Discovery: A Review and Bibliometric Analysis

Abstract

The drug discovery process is highly intricate and complex. Driven by unprecedented advances in AI technology, the application of artificial intelligence in drug discovery (AIDD) is showing significant growth, and AIDD has the potential to fundamentally change and revolutionize traditional drug development models in the foreseeable future. We selected 7061 literature studies on artificial intelligence used in drug discovery from the Web of Science Core Collection database from 2000 to 2024, and adopted bibliometric tools, such as Citespace, VOSviewer, and RStudio, to select nodes for knowledge graph generation and visualization analysis by using country, institution, author, and keywords. The results showed that from 2017 to 2024, the literature on the use of artificial intelligence for drug discovery exploded, with the United States having the largest number of papers, and China's number of papers growing rapidly and surpassing the United States in the last two years. Molecular docking, virtual screening, algorithm optimization, interpretable AIDD, protein language modeling, drug targets, and protein interaction prediction were found to be the research hotspots in this field in recent years. AI has been widely used in the field of drug research and development. Based on the quantitative analysis of the literature on the use of artificial intelligence in drug discovery, this paper has revealed the current research status in this field, clarified the current research hotspots and future research trends, and provided certain references for researchers to plan future research directions.

Ähnliche Arbeiten