Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

Liu, Shichao; Zhang, Yang; Cui, Yuxin; Yang, Qian; Deng, Yifan; Zhang, Zhongfei Mark; Zhang, Wen

doi:10.1109/tcbb.2022.3172421

Cited by 44 publications

(16 citation statements)

References 49 publications

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“…To address how to focus on integrating multiple drug features to predict unknown DDI, Zhang et al. [57] proposed a model DANN‐DDI for enhancing DDI prediction using deep attention neural networks. Attention neural network is used in drug pair feature learning.…”

Section: Multimodal‐based Methodsmentioning

confidence: 99%

Deep learning for drug‐drug interaction prediction: A comprehensive review

Li,

Xiong,

Zhang

et al. 2024

Quant. Biol.

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The prediction of drug‐drug interactions (DDIs) is a crucial task for drug safety research, and identifying potential DDIs helps us to explore the mechanism behind combinatorial therapy. Traditional wet chemical experiments for DDI are cumbersome and time‐consuming, and are too small in scale, limiting the efficiency of DDI predictions. Therefore, it is particularly crucial to develop improved computational methods for detecting drug interactions. With the development of deep learning, several computational models based on deep learning have been proposed for DDI prediction. In this review, we summarized the high‐quality DDI prediction methods based on deep learning in recent years, and divided them into four categories: neural network‐based methods, graph neural network‐based methods, knowledge graph‐based methods, and multimodal‐based methods. Furthermore, we discuss the challenges of existing methods and future potential perspectives. This review reveals that deep learning can significantly improve DDI prediction performance compared to traditional machine learning. Deep learning models can scale to large‐scale datasets and accept multiple data types as input, thus making DDI predictions more efficient and accurate.

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Section: Multimodal‐based Methodsmentioning

confidence: 99%

Deep learning for drug‐drug interaction prediction: A comprehensive review

Li,

Xiong,

Zhang

et al. 2024

Quant. Biol.

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“…This method combines each drug embedding vector in all event types using (np.concatenate). Then the multiplication of the vectors of the drug pairs was performed using the multiplication method used in the article 39 0.7386…”

Section: (B)mentioning

confidence: 99%

“…The DANN-DDI model after constructing multiple drug feature networks adopts an attention neural network to aggregate the learned drug representations and predict drug-drug interactions. We implement DANN-DDI according to the descriptions in 39 . CNN-DDI model first gathers the feature vectors from interaction matrices and calculates drug similarity.…”

Section: (B)mentioning

confidence: 99%

Prediction of drug-drug interaction events using graph neural networks based feature extraction

Al-Rabeah

Lakizadeh

2022

Sci Rep

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The prevalence of multi_drug therapies has been increasing in recent years, particularly among the elderly who are suffering from several diseases. However, unexpected Drug_Drug interaction (DDI) can cause adverse reactions or critical toxicity, which puts patients in danger. As the need for multi_drug treatment increases, it's becoming increasingly necessary to discover DDIs. Nevertheless, DDIs detection in an extensive number of drug pairs, both in-vitro and in-vivo, is costly and laborious. Therefore, DDI identification is one of the most concerns in drug-related researches. In this paper, we propose GNN-DDI, a deep learning-based method for predicting DDI-associated events in two stages. In the first stage, we collect the drugs information from different sources and then integrate them through the formation of an attributed heterogeneous network and generate a drug embedding vector based on different drug interaction types and drug attributes. In the second stage, we aggregate the representation vectors then predictions of the DDIs and their events are performed through a deep multi-model framework. Various evaluation results show that the proposed method can outperform state-of-the methods in the prediction of drug-drug interaction-associated events. The experimental results indicate that producing the drug's representations based on different drug interaction types and attributes is efficient and effective and can better show the intrinsic characteristics of a drug.

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“…Feng et al [ 25 ] applied deep graph auto-encoder to learn latent drugs representations fed to a deep feedforward neural network for DDIs prediction. Liu et al [ 27 ] introduced a deep attention neural network framework for drug-drug interaction prediction, which can effectively integrate multiple drug features. For adverse drug-drug interaction (ADDI), Zhu et al [ 28 ] employed eight attributes and developed a discriminative learning algorithm to learn attribute representations of each adverse drug pair for exploiting their consensus and complementary information in multi-attribute ADDI prediction.…”

Section: Introductionmentioning

confidence: 99%

A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

Lei

2023

PLoS Comput Biol

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Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug’s unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN–DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling.

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Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks

Cited by 44 publications

References 49 publications

Deep learning for drug‐drug interaction prediction: A comprehensive review

Deep learning for drug‐drug interaction prediction: A comprehensive review

Prediction of drug-drug interaction events using graph neural networks based feature extraction

A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

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