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

Authors :
Sunyong Yoo
Hyung Chae Yang
Seongyeong Lee
Jaewook Shin
Seyoung Min
Eunjoo Lee
Minkeun Song
Doheon Lee
Source :
Frontiers in Pharmacology, Vol 11 (2020)
Publication Year :
2020
Publisher :
Frontiers Media S.A., 2020.

Abstract

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.

Details

Language :
English
ISSN :
16639812
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Pharmacology
Publication Type :
Academic Journal
Accession number :
edsdoj.f4a0a08e3c24c12a0d504a95311efeb
Document Type :
article
Full Text :
https://doi.org/10.3389/fphar.2020.584875