2025, Vol. 13, Issue 3, Part C

author(s)
Reshma Khan and Kanwal Preet Singh Atwal

abstract
The study examines the
growing role of computational methodologies, particularly machine learning, in
the analysis and research of medicinal plants. With advancements in
bioinformatics and data-driven techniques, the study aims to assess how these
tools are transforming the identification, classification, and drug discovery
processes related to medicinal plants. The methodology involves a bibliometric
analysis, using data from academic articles to track trends, identify
influential authors, institutions, and countries, and evaluate international
collaborations. The study highlights prominent keywords such as “medicinal
plants,” “machine learning,” and “feature extraction,” demonstrating the
integration of computational methods in biological research. By analyzing
citation and co-authorship patterns, the research identifies key contributors,
including India, which emerges as a leading country in this field.
Additionally, academic institutions, particularly in India, play a significant
role in advancing this interdisciplinary approach. The findings emphasize the
importance of international collaboration in driving research forward,
suggesting that stronger, more interconnected academic networks could enhance
the global impact of medicinal plant research. Ultimately, this study
underscores the potential of machine learning and computational methods in
revolutionizing the study of medicinal plants and accelerating the discovery of
novel bioactive compounds for drug development.