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

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, two training datasets were prepared from literature on antibacterial essential oils to classify active/inactive oils. Random forest classifiers constructed from the data showed improved prediction performance in both test datasets. Prior feature selection using hierarchical information criterion further improved the performance. Furthermore, an antibacterial assay using a standard strain of Staphyloc

SUBMITTER: Yabuuchi H 

PROVIDER: S-EPMC11217266 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature