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Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) ex...

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Autores principales: Yan, Xiao-Ying, Yin, Peng-Wei, Wu, Xiao-Meng, Han, Jia-Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8721167/
https://www.ncbi.nlm.nih.gov/pubmed/34987405
http://dx.doi.org/10.3389/fphar.2021.794205
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author Yan, Xiao-Ying
Yin, Peng-Wei
Wu, Xiao-Meng
Han, Jia-Xin
author_facet Yan, Xiao-Ying
Yin, Peng-Wei
Wu, Xiao-Meng
Han, Jia-Xin
author_sort Yan, Xiao-Ying
collection PubMed
description Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.
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spelling pubmed-87211672022-01-04 Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks Yan, Xiao-Ying Yin, Peng-Wei Wu, Xiao-Meng Han, Jia-Xin Front Pharmacol Pharmacology Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types. Frontiers Media S.A. 2021-12-20 /pmc/articles/PMC8721167/ /pubmed/34987405 http://dx.doi.org/10.3389/fphar.2021.794205 Text en Copyright © 2021 Yan, Yin, Wu and Han. https://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
Yan, Xiao-Ying
Yin, Peng-Wei
Wu, Xiao-Meng
Han, Jia-Xin
Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks
title Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks
title_full Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks
title_fullStr Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks
title_full_unstemmed Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks
title_short Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks
title_sort prediction of the drug–drug interaction types with the unified embedding features from drug similarity networks
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8721167/
https://www.ncbi.nlm.nih.gov/pubmed/34987405
http://dx.doi.org/10.3389/fphar.2021.794205
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