Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa 58051-900, Brazil.
Pharmacy Department, Faculty of Natural and Exact Sciences, University of Oriente, Santiago de Cuba 90500, Cuba.
Molecules. 2022 Feb 17;27(4):1366. doi: 10.3390/molecules27041366.
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.
精油（EOs）是一种化学化合物的混合物，在食品、化妆品、香水、农业和制药行业有着悠久的应用历史。本研究的主要目的是使用不同的机器学习算法，在 45 种精油和抗原生动物活性（抗疟原虫、抗利什曼原虫和抗锥虫）之间找到化学模式。在分析中，使用了 45 种精油样本，采用了无监督的自组织映射（SOM）和有监督的随机森林（RF）方法。在生成的图谱中，命中率高于 70%，结果表明，使用有监督和无监督的机器学习方法可以找到化学模式。共鉴定出 20 种化合物（19 种萜类化合物和一种含硫化合物），并与文献报道进行了比较。这些模型可用于更有效地研究和筛选具有抗原生动物活性的精油，并且所需时间和资金成本更低。
