Molecules, Vol. 26, Pages 6279: Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition –Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils
Molecules, Vol. 26, Pages 6279: Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition–Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils
Molecules doi: 10.3390/molecules26206279
Authors:
Alessio Ragno
Anna Baldisserotto
Lorenzo Antonini
Manuela Sabatino
Filippo Sapienza
Erika Baldini
Raissa Buzzi
Silvia Vertuani
Stefano Manfredini
Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition–activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils’ chemical compositions and biological d...
Source: Molecules - Category: Chemistry Authors: Alessio Ragno Anna Baldisserotto Lorenzo Antonini Manuela Sabatino Filippo Sapienza Erika Baldini Raissa Buzzi Silvia Vertuani Stefano Manfredini Tags: Article Source Type: research