Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches
<b>Objectives</b>: This study implements a multi-dimensional methodology to systematically identify potential natural antibiotics derived from the medicinal plants utilized in Ayurvedic practices. <b>Methods</b>: Two primary analytical techniques are employed to explore the a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/18/2/192 |
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| author | Pei Gao Ahmad Kamal Nasution Naoaki Ono Shigehiko Kanaya Md. Altaf-Ul-Amin |
| author_facet | Pei Gao Ahmad Kamal Nasution Naoaki Ono Shigehiko Kanaya Md. Altaf-Ul-Amin |
| author_sort | Pei Gao |
| collection | DOAJ |
| description | <b>Objectives</b>: This study implements a multi-dimensional methodology to systematically identify potential natural antibiotics derived from the medicinal plants utilized in Ayurvedic practices. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| format | Article |
| id | doaj-art-c9bb79cddec24360a93632191a34d6a0 |
| institution | DOAJ |
| issn | 1424-8247 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj-art-c9bb79cddec24360a93632191a34d6a02025-08-20T02:44:39ZengMDPI AGPharmaceuticals1424-82472025-01-0118219210.3390/ph18020192Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning ApproachesPei Gao0Ahmad Kamal Nasution1Naoaki Ono2Shigehiko Kanaya3Md. Altaf-Ul-Amin4Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikoma 630-0101, Nara, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikoma 630-0101, Nara, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikoma 630-0101, Nara, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikoma 630-0101, Nara, JapanGraduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Ikoma 630-0101, Nara, Japan<b>Objectives</b>: This study implements a multi-dimensional methodology to systematically identify potential natural antibiotics derived from the medicinal plants utilized in Ayurvedic practices. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/1424-8247/18/2/192Ayurvedicmedicinal plantsnetwork analysismachine learning |
| spellingShingle | Pei Gao Ahmad Kamal Nasution Naoaki Ono Shigehiko Kanaya Md. Altaf-Ul-Amin Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches Pharmaceuticals Ayurvedic medicinal plants network analysis machine learning |
| title | Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches |
| title_full | Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches |
| title_fullStr | Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches |
| title_full_unstemmed | Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches |
| title_short | Investigating Potential Anti-Bacterial Natural Products Based on Ayurvedic Formulae Using Supervised Network Analysis and Machine Learning Approaches |
| title_sort | investigating potential anti bacterial natural products based on ayurvedic formulae using supervised network analysis and machine learning approaches |
| topic | Ayurvedic medicinal plants network analysis machine learning |
| url | https://www.mdpi.com/1424-8247/18/2/192 |
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