Structure-based chemical ontology improves chemometric prediction of antibacterial essential oils

Author:

Yabuuchi Hiroaki1,Fujiwara Makiko1,Shigemoto Akihiko2,Hayashi Kazuhito1,Nomura Yuhei2,Nakashima Mayumi2,Ogusu Takeshi1,Mori Megumi1,Tokumoto Shin-ichi2,Miyai Kazuyuki1

Affiliation:

1. Department of Pharmaceutical Industry, Industrial Technology Center of Wakayama Prefecture

2. Department of Digital Manufacturing, Industrial Technology Center of Wakayama Prefecture

Abstract

Abstract Plants are valuable resources for drug discovery as they produce diverse bioactive compounds. However, the chemical diversity makes it difficult to predict the biological activity of plant extracts via conventional chemometric methods. In this research, we propose a new computational model that integrates chemical composition data with structure-based chemical ontology. For a model validation, a training dataset was prepared from literature on antibacterial essential oils to classify active/inactive oils. A random forest classifier constructed from the data showed improved prediction performance in a test dataset. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphylococcus aureus revealed that the classifier correctly predicted the activity of commercially available oils with an accuracy of 83% (= 10/12). The results of this study indicate that machine learning of chemical composition data integrated with chemical ontology can be a highly efficient approach for exploring bioactive plant extracts.

Publisher

Research Square Platform LLC

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