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A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds
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 meth...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7845697/ https://www.ncbi.nlm.nih.gov/pubmed/33519445 http://dx.doi.org/10.3389/fphar.2020.584875 |
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author | Yoo, Sunyong Yang, Hyung Chae Lee, Seongyeong Shin, Jaewook Min, Seyoung Lee, Eunjoo Song, Minkeun Lee, Doheon |
author_facet | Yoo, Sunyong Yang, Hyung Chae Lee, Seongyeong Shin, Jaewook Min, Seyoung Lee, Eunjoo Song, Minkeun Lee, Doheon |
author_sort | Yoo, Sunyong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7845697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78456972021-01-30 A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds Yoo, Sunyong Yang, Hyung Chae Lee, Seongyeong Shin, Jaewook Min, Seyoung Lee, Eunjoo Song, Minkeun Lee, Doheon Front Pharmacol Pharmacology 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. Frontiers Media S.A. 2020-11-30 /pmc/articles/PMC7845697/ /pubmed/33519445 http://dx.doi.org/10.3389/fphar.2020.584875 Text en Copyright © 2020 Yoo, Yang, Lee, Shin, Min, Lee, Song and Lee. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Pharmacology Yoo, Sunyong Yang, Hyung Chae Lee, Seongyeong Shin, Jaewook Min, Seyoung Lee, Eunjoo Song, Minkeun Lee, Doheon A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds |
title | A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds |
title_full | A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds |
title_fullStr | A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds |
title_full_unstemmed | A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds |
title_short | A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds |
title_sort | deep learning-based approach for identifying the medicinal uses of plant-derived natural compounds |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7845697/ https://www.ncbi.nlm.nih.gov/pubmed/33519445 http://dx.doi.org/10.3389/fphar.2020.584875 |
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