KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction

Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical application, and effectively identifying potential DDIs during clinical trials is critical for patients and society. Most of existing computational models with AI techniques often concentrate on integrating multiple data sources and combining popular embedding methods together. Yet, researchers pay less attention to the potential correlations between drug and other entities such as targets and genes. Moreover, recent studies also adopted knowledge graph (KG) for DDI prediction. Yet, this line of methods learn node latent embedding directly, but they are limited in obtaining the rich neighborhood information of each entity in the KG. To address the above limitations, we propose an end-to-end framework, called Knowledge Graph Neural Network (KGNN), to resolve the DDI prediction. Our framework can effectively capture drug and its potential neighborhoods by mining their associated relations in KG. To extract both high-order structures and semantic relations of the KG, we learn from the neighborhoods for each entity in the KG as their local receptive, and then integrate neighborhood information with bias from representation of the current entity. This way, the receptive field can be naturally extended to multiple hops away to model high-order topological information and to obtain drugs potential long-distance correlations. We have implemented our method and conducted experiments based on several widely-used datasets. Empirical results show that KGNN outperforms the classic and state-of-the-art models.

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Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

10.1101/2021.03.16.435553 ◽

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.

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Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

Scientific Reports ◽

10.1038/s41598-019-50121-3 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 10

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.

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Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6508 ◽

2020 ◽

Vol 34 (05) ◽

pp. 9612-9619

Author(s):

Zhao Zhang ◽

Fuzhen Zhuang ◽

Hengshu Zhu ◽

Zhiping Shi ◽

Hui Xiong ◽

...

Keyword(s):

Neural Network ◽

Missing Values ◽

State Of The Art ◽

Attention Mechanism ◽

Knowledge Graph ◽

Incomplete Knowledge ◽

Neighborhood Information ◽

Local Neighborhood ◽

Proposed Model ◽

Knowledge Graphs

The rapid proliferation of knowledge graphs (KGs) has changed the paradigm for various AI-related applications. Despite their large sizes, modern KGs are far from complete and comprehensive. This has motivated the research in knowledge graph completion (KGC), which aims to infer missing values in incomplete knowledge triples. However, most existing KGC models treat the triples in KGs independently without leveraging the inherent and valuable information from the local neighborhood surrounding an entity. To this end, we propose a Relational Graph neural network with Hierarchical ATtention (RGHAT) for the KGC task. The proposed model is equipped with a two-level attention mechanism: (i) the first level is the relation-level attention, which is inspired by the intuition that different relations have different weights for indicating an entity; (ii) the second level is the entity-level attention, which enables our model to highlight the importance of different neighboring entities under the same relation. The hierarchical attention mechanism makes our model more effective to utilize the neighborhood information of an entity. Finally, we extensively validate the superiority of RGHAT against various state-of-the-art baselines.

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