Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites
Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/4/1366 |
| _version_ | 1797477576205139968 |
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| author | Renata Priscila Barros de Menezes Luciana Scotti Marcus Tullius Scotti Jesús García Rosalia González Lianet Monzote William N. Setzer |
| author_facet | Renata Priscila Barros de Menezes Luciana Scotti Marcus Tullius Scotti Jesús García Rosalia González Lianet Monzote William N. Setzer |
| author_sort | Renata Priscila Barros de Menezes |
| collection | DOAJ |
| description | Essential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost. |
| first_indexed | 2024-03-09T21:20:43Z |
| format | Article |
| id | doaj.art-4f20df108f02430c94cfb64f663d1f32 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T21:20:43Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-4f20df108f02430c94cfb64f663d1f322023-11-23T21:22:55ZengMDPI AGMolecules1420-30492022-02-01274136610.3390/molecules27041366Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa ParasitesRenata Priscila Barros de Menezes0Luciana Scotti1Marcus Tullius Scotti2Jesús García3Rosalia González4Lianet Monzote5William N. Setzer6Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, BrazilPost-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, BrazilPost-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, BrazilPharmacy Department, Faculty of Natural and Exact Sciences, University of Oriente, Santiago de Cuba 90500, CubaToxicology and Biomedicine Centre (TOXIMED), University of Medical Science, Santiago de Cuba 90500, CubaParasitology Department, Institute of Tropical Medicine “Pedro Kouri”, Havana 10400, CubaAromatic Plant Research Center, 230 N 1200 E, Suite 100, Lehi, UT 84043, USAEssential oils (EOs) are a mixture of chemical compounds with a long history of use in food, cosmetics, perfumes, agricultural and pharmaceuticals industries. The main object of this study was to find chemical patterns between 45 EOs and antiprotozoal activity (antiplasmodial, antileishmanial and antitrypanosomal), using different machine learning algorithms. In the analyses, 45 samples of EOs were included, using unsupervised Self-Organizing Maps (SOM) and supervised Random Forest (RF) methodologies. In the generated map, the hit rate was higher than 70% and the results demonstrate that it is possible find chemical patterns using a supervised and unsupervised machine learning approach. A total of 20 compounds were identified (19 are terpenes and one sulfur-containing compound), which was compared with literature reports. These models can be used to investigate and screen for bioactivity of EOs that have antiprotozoal activity more effectively and with less time and financial cost.https://www.mdpi.com/1420-3049/27/4/1366essential oilCuban plantsmachine learning analysisantiprotozoal activity |
| spellingShingle | Renata Priscila Barros de Menezes Luciana Scotti Marcus Tullius Scotti Jesús García Rosalia González Lianet Monzote William N. Setzer Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites Molecules essential oil Cuban plants machine learning analysis antiprotozoal activity |
| title | Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites |
| title_full | Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites |
| title_fullStr | Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites |
| title_full_unstemmed | Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites |
| title_short | Machine Learning Analysis of Essential Oils from Cuban Plants: Potential Activity against Protozoa Parasites |
| title_sort | machine learning analysis of essential oils from cuban plants potential activity against protozoa parasites |
| topic | essential oil Cuban plants machine learning analysis antiprotozoal activity |
| url | https://www.mdpi.com/1420-3049/27/4/1366 |
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