scholarly journals MDNN: A Multimodal Deep Neural Network for Predicting Drug-Drug Interaction Events

2021 ◽  
Author(s):  
Tengfei Lyu ◽  
Jianliang Gao ◽  
Ling Tian ◽  
Zhao Li ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Deep Neural Network ◽  
Multimodal Fusion ◽  
Drug Knowledge ◽  
Appropriate Therapy ◽  
Multimodal Representations ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Heterogeneous Feature

The interaction of multiple drugs could lead to serious events, which causes injuries and huge medical costs. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. Recently, many AI-based techniques have been proposed for predicting DDI associated events. However, most existing methods pay less attention to the potential correlations between DDI events and other multimodal data such as targets and enzymes. To address this problem, we propose a Multimodal Deep Neural Network (MDNN) for DDI events prediction. In MDNN, we design a two-pathway framework including drug knowledge graph (DKG) based pathway and heterogeneous feature (HF) based pathway to obtain drug multimodal representations. Finally, a multimodal fusion neural layer is designed to explore the complementary among the drug multimodal representations. We conduct extensive experiments on real-world dataset. The results show that MDNN can accurately predict DDI events and outperform the state-of-the-art models.

SmileGNN: Drug-Drug Interaction Prediction Based on SMILES and Graph Neural Network

2021 ◽  
Author(s):  
Xueting Han ◽  
Xutao Li ◽  
Junyi Li
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Prediction Model ◽  
Structural Features ◽  
Interaction Prediction ◽  
Topological Features ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Interaction Between Drugs

Abstract Background: The use of multiple drugs at the same time can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. But it is difficult to test the drug-drug interaction widely and effectively before the drug is put into market. Therefore, the prediction of drug-drug interaction has become an important research in biomedical field.Results: In recent years, researchers have used deep learning to predict drug-drug interaction by using drug structural features and graph theory, and they have achieved a series of achievements. A drug-drug interaction prediction model SmileGNN is proposed in this paper. The structural features of drugs are constructed by using SMILES data. The topological features of drugs in knowledge graph are obtained by graph neural network. The structural and topological features of drugs are aggregated to predict the interaction of new drug pairs. Conclusions: The experimental results show that the model proposed in this paper combines a variety of data sources, and has better prediction performance compared with the existing prediction model of drug-drug interaction prediction. The most striking result is that five out of top ten predicted new interaction of drugs are verified from the latest database, which proves the credibility of SmileGNN.

scholarly journals Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

2021 ◽  
Author(s):  
Shichao Liu ◽  
Yang Zhang ◽  
Yuxin Cui ◽  
Yang Qiu ◽  
Yifan Deng ◽  
...  
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Data Sources ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Multiple Data ◽  
Drug Drug Interaction ◽  
Multiple Drugs ◽  
Novel Drug

AbstractDrug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are c o-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

scholarly journals Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Narjes Rohani ◽  
Changiz Eslahchi
Keyword(s):  
Neural Network ◽  
Drug Interaction ◽  
Side Effect ◽  
Network Architecture ◽  
Selection Process ◽  
Superior Performance ◽  
Multiple Drug ◽  
Interaction Prediction ◽  
Benchmark Datasets ◽  
Drug Drug Interaction

Abstract Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.

scholarly journals Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Remzi Celebi ◽  
Huseyin Uyar ◽  
Erkan Yasar ◽  
Ozgur Gumus ◽  
Oguz Dikenelli ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Development ◽  
Drug Interactions ◽  
Pairwise Disjoint ◽  
Cross Validation ◽  
Knowledge Graph ◽  
Paired Data ◽  
Post Marketing Surveillance ◽  
Drug Knowledge ◽  
Drug Drug Interaction

Abstract Background Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs. Results We designed different evaluation settings to accurately assess the performance for predicting DDIs. The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. We have applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used. Conclusion We showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We addressed the evaluation biases by introducing drug-wise and pairwise disjoint test classes. Although the performance scores for drug-wise and pairwise disjoint seem to be low, the results can be considered to be realistic in predicting the interactions for drugs with limited interaction information.

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