A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds

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Medicinal plants and their extracts have been used as important sources for drug discovery. In particular, plant-derived natural compounds, including phytochemicals, antioxidants, vitamins, and minerals, are gaining attention as they promote health and prevent disease. Although several in vitro methods have been developed to confirm the biological activities of natural compounds, there is still considerable room to reduce time and cost. To overcome these limitations, several in silico methods have been proposed for conducting large-scale analysis, but they are still limited in terms of dealing with incomplete and heterogeneous natural compound data. Here, we propose a deep learning-based approach to identify the medicinal uses of natural compounds by exploiting massive and heterogeneous drug and natural compound data. The rationale behind this approach is that deep learning can effectively utilize heterogeneous features to alleviate incomplete information. Based on latent knowledge, molecular interactions, and chemical property features, we generated 686 dimensional features for 4,507 natural compounds and 2,882 approved and investigational drugs. The deep learning model was trained using the generated features and verified drug indication information. When the features of natural compounds were applied as input to the trained model, potential efficacies were successfully predicted with high accuracy, sensitivity, and specificity.
Publisher
FRONTIERS MEDIA SA
Issue Date
2020-11
Language
English
Article Type
Article
Citation

FRONTIERS IN PHARMACOLOGY, v.11

ISSN
1663-9812
DOI
10.3389/fphar.2020.584875
URI
http://hdl.handle.net/10203/279455
Appears in Collection
BiS-Journal Papers(저널논문)
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