From Molecules to Models: A Bibliometric Analysis of Visualization in AI-Driven Chemistry with Emerging Educational Implications
DOI:
https://doi.org/10.59247/ijase.v4i2.156Keywords:
Artificial Intelligence, Chemical Visualization, Molecular Modeling, Bibliometric AnalysisAbstract
The rapid advancement of artificial intelligence has significantly transformed chemical research, particularly in molecular modeling, reaction prediction, and visualization. However, the role of visualization within AI-driven chemistry remains fragmented and underexplored. This study addresses this gap through a bibliometric analysis of research at the intersection of artificial intelligence, chemistry, and visualization. Data were collected from the Scopus database in April 2026 using a structured search query and screened following PRISMA guidelines. The final dataset consisted of 1,069 journal articles, which were analyzed using Biblioshiny and VOSviewer to examine publication trends, conceptual structures, and thematic evolution. To complement the bibliometric findings, eight additional articles related to IBM RXN for Chemistry were qualitatively reviewed to explore the role of emerging AI platforms in chemical visualization. The results reveal a substantial increase in research output after 2019, reflecting the rapid expansion of AI applications in chemistry. The field is dominated by themes related to machine learning, molecular modeling, computational chemistry, and analytical techniques. Temporal analysis indicates a progression from early computational approaches to advanced AI-based methods, including deep learning and predictive modeling. Rather than emerging as an independent research theme, visualization is primarily embedded within machine learning, molecular modeling, retrosynthesis, and computational chemistry workflows, where it supports the interpretation of complex chemical data and predictive outputs. The analysis of IBM RXN-related studies further highlights the emergence of integrated platforms that combine molecular design, reaction prediction, and retrosynthesis within interactive visualization environments. Overall, AI-driven chemistry is undergoing a transition toward platform-based and predictive visualization systems. These findings contribute to understanding how visualization supports knowledge generation in contemporary chemistry, although citation-based indicators for the most recent publications should be interpreted cautiously due to the inclusion of records indexed up to April 2026.
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