Pei Gao, Ahmad Kamal Nasution, Naoaki Ono, Shigehiko Kanaya, Md Altaf-Ul-Amin
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引用次数: 0
Abstract
Objectives: This study implements a multi-dimensional methodology to systematically identify potential natural antibiotics derived from the medicinal plants utilized in Ayurvedic practices. Methods: Two primary analytical techniques are employed to explore the antibiotic potential of the medicinal plants. The initial approach utilizes a supervised network analysis, which involves the application of distance measurement algorithms to scrutinize the interconnectivity and relational patterns within the network derived from Ayurvedic formulae. Results: 39 candidate plants with potential natural antibiotic properties were identified. The second approach leverages advanced machine learning techniques, particularly focusing on feature extraction and pattern recognition. This approach yielded a list of 32 plants exhibiting characteristics indicative of natural antibiotics. A key finding of this research is the identification of 17 plants that were consistently recognized by both analytical methods. These plants are well-documented in existing literature for their antibacterial properties, either directly or through their bioactive compounds, which suggests a strong validation of the study's methodology. By synergizing network analysis with machine learning, this study provides a rigorous and multi-faceted examination of Ayurvedic medicinal plants, significantly contributing to the identification of natural antibiotic candidates. Conclusions: This research not only reinforces the potential of traditional medicine as a source for new therapeutics but also demonstrates the effectiveness of combining classical and contemporary analytical techniques to explore complex biological datasets.
PharmaceuticalsPharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
6.10
自引率
4.30%
发文量
1332
审稿时长
6 weeks
期刊介绍:
Pharmaceuticals (ISSN 1424-8247) is an international scientific journal of medicinal chemistry and related drug sciences.Our aim is to publish updated reviews as well as research articles with comprehensive theoretical and experimental details. Short communications are also accepted; therefore, there is no restriction on the maximum length of the papers.