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

Al-Rabeah, Mohammad Hussain; Lakizadeh, Amir

doi:10.1038/s41598-022-19999-4

Cited by 21 publications

(7 citation statements)

References 45 publications

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“…Drug features may be correlated and contain redundant information that makes it challenging to effectively integrate the various features, to alleviate this limitation, Lakizadeh et al. [62] performed DDI prediction in two stages and proposed a deep learning model GNN‐DDI using GNN to integrate multiple drug features. First, the similarity matrices are constructed using four different drug feature matrices, and then the attribute heterogeneous networks are constructed using the DDI graph and the four similarity matrices, with one similarity matrix used as the node attribute in each step.…”

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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“…DrugBank is a comprehensive database that provides information about 12 151 drugs, including experimental and FDA-approved drugs ( Knox et al 2011 , Deng et al 2020 , Al-Rabeah and Lakizadeh 2022 , Lakizadeh and Babaei 2022 ). KEGG is an integrated database for biological interpretation ( Knox et al 2011 , Kanehisa and Goto 2000 , Kanehisa et al 2017 , Ryu et al 2018 , Hou et al 2019 ).…”

Section: Materials and Methodologymentioning

confidence: 99%

“…A general source for known DDIs is through the experiments ( Tari et al 2010 , Kim and Nam 2022 ). However, identifying polypharmacy side effects in vitro and in vivo is impractical in terms of cost and time ( Rohani and Eslahchi 2019 , Deng et al 2020 , Feng et al 2020 , Al-Rabeah and Lakizadeh 2022 , Kim and Nam 2022 ). Thus, many computational models are developed to detect DDIs.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, similarity-based methods assume that similar drugs may interact with the same drug ( Mitchell 2005 , Peterson 2009 , Zhang et al 2017 , Zitnik et al 2018 ). The GNN–DDI method generated a drug embedding vector and used a graph neural network to predict polypharmacy side effects prediction ( Al-Rabeah and Lakizadeh 2022 ). Lin proposed the MDF–SA–DDI method based on multi-source drug fusion, multi-source feature fusion, and transformer self-attention mechanism to perform latent feature fusion ( Lin et al 2022b ).…”

Section: Introductionmentioning

confidence: 99%

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DPSP: a multimodal deep learning framework for polypharmacy side effects prediction

Masumshah,

Eslahchi

2023

Bioinformatics Advances

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Motivation Because unanticipated Drug-Drug Interactions (DDIs) can result in severe bodily harm, identifying the adverse effects of polypharmacy is one of the most important tasks in human health. Over the past few decades, computational methods for predicting the adverse effects of polypharmacy have been developed. Results This article presents DPSP, a framework for predicting polypharmacy side effects based on the construction of novel drug features and the application of a deep neural network to predict DDIs. In the first step, a variety of drug information is evaluated, and a feature extraction method and the Jaccard similarity are used to determine similarities between two drugs. By combining these similarities, a novel feature vector is generated for each drug. In the second step, the method predicts DDIs for specific DDI events using a multimodal framework and drug feature vectors. On three benchmark datasets, the performance of DPSP is measured by comparing its results to those of several well-known methods, such as GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, DNN, DeepDDI, KNN, LR, and RF. DPSP outperforms these classification methods based on a variety of classification metrics. The results indicate that the use of diverse drug information is effective and efficient for identifying DDI adverse effects. Availability The source code and datasets are available at https://github.com/raziyehmasumshah/DPSP. Supplementary information Supplementary data are available at Bioinformatics Advances online.

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“…GNNs offer a promising alternative by providing a way to encode the structural and relational information of molecules in a graph-based representation that can be easily processed by a neural network. They have been applied to tasks such as predicting the efficacy of potential drug molecules and identifying potential drug-drug interactions [9,15,16].…”

Section: Introductionmentioning

confidence: 99%

Estimating protein-ligand interactions with geometric deep learning and mixture density models

Kalakoti,

Gawande,

Sundar

2023

Preprint

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Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for anyin-silicodrug-design workflow. Multiple experimental and computational methods have been developed to better understand these intermolecular interactions. With the availability of a large number of structural datasets, there is a need for developing statistical frameworks that improve upon existing physics-based solutions. Here, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. A technique to generate graphical representations of protein was developed to exploit the topological and electrostatic properties of the binding region. The developed framework, based on graph neural networks, learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand–target pair. This potential can be coupled with global optimization algorithms such as differential evolution to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.Significance statementCurrent machine learning-based solutions to model protein-ligand interactions lack the level of interpretability that physics-based methods usually provide. Here, a workflow to embed protein binding surfaces as graphs was developed to serve as a viable data structure to be processed by geometric deep learning. The developed architecture based on mixture density models was employed to accurately estimate the position and conformation of the small molecule within the binding region. The likelihood-based scoring function was compared against existing physics-based alternatives, and significant performance improvements in terms of docking power, screening power and reverse screening power were observed. Taken together, the developed framework provides a platform for utilising geometric deep-learning models for interpretable prediction of protein-ligand interactions at a residue level.

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

Cited by 21 publications

References 45 publications

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

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

DPSP: a multimodal deep learning framework for polypharmacy side effects prediction

Estimating protein-ligand interactions with geometric deep learning and mixture density models

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