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

Masumshah, Raziyeh; Eslahchi, Changiz

doi:10.1093/bioadv/vbad110

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…DPSP [ 51 ]. Masumshah et al introduced a deep learning framework for predicting multiple drug side effects, divided into two steps.…”

Section: Methodsmentioning

confidence: 99%

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention

He,

Yun,

2024

J Transl Med

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Background Accurately identifying the risk level of drug combinations is of great significance in investigating the mechanisms of combination medication and adverse reactions. Most existing methods can only predict whether there is an interaction between two drugs, but cannot directly determine their accurate risk level. Methods In this study, we propose a multi-class drug combination risk prediction model named AERGCN-DDI, utilizing a relational graph convolutional network with a multi-head attention mechanism. Drug-drug interaction events with varying risk levels are modeled as a heterogeneous information graph. Attribute features of drug nodes and links are learned based on compound chemical structure information. Finally, the AERGCN-DDI model is proposed to predict drug combination risk level based on heterogenous graph neural network and multi-head attention modules. Results To evaluate the effectiveness of the proposed method, five-fold cross-validation and ablation study were conducted. Furthermore, we compared its predictive performance with baseline models and other state-of-the-art methods on two benchmark datasets. Empirical studies demonstrated the superior performances of AERGCN-DDI. Conclusions AERGCN-DDI emerges as a valuable tool for predicting the risk levels of drug combinations, thereby aiding in clinical medication decision-making, mitigating severe drug side effects, and enhancing patient clinical prognosis.

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“…DPSP [ 51 ]. Masumshah et al introduced a deep learning framework for predicting multiple drug side effects, divided into two steps.…”

Section: Methodsmentioning

confidence: 99%

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention

He,

Yun,

2024

J Transl Med

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“…Consequently, predicting DDI solely based on drug molecular diagram structures may lead to insufficient accuracy. There are also innovative methods that utilize multimodal data or drug interaction information for prediction, yielding superior results, DPSP[ 23 ] predicts DDIs using a multimodal framework through drug substructure information as well as mono side effects, target proteins, enzymes, and pathways. NNPS [ 24 ] Predicts polypharmacy side effects by using novel feature vectors based on mono side effects, and drug–protein interaction information.…”

Section: Introductionmentioning

confidence: 99%

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug–drug interaction prediction

Zhu,

Che,

Jiang

et al. 2024

BMC Bioinformatics

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Background Drug–drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. Results In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. Conclusion The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.

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“…Models relying on large-scale self-supervised pretraining like MG-BERT [ 5 ] may exhibit more robust performance under scenarios with limited labeled data. Additionally, models like NNPS [ 3 ], DSDP [ 4 ], and DRWBNCF [ 12 ], which utilize the biological profile of small molecules, provide valuable and associative information for downstream tasks such as drug repositioning and drug–drug interaction prediction. Each method has its unique strengths and weaknesses, tailored to different application contexts.…”

Section: Introductionmentioning

confidence: 99%

Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining

Xiang,

Zhong,

et al. 2024

Briefings in Bioinformatics

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Accurate prediction of molecular properties is fundamental in drug discovery and development, providing crucial guidance for effective drug design. A critical factor in achieving accurate molecular property prediction lies in the appropriate representation of molecular structures. Presently, prevalent deep learning–based molecular representations rely on 2D structure information as the primary molecular representation, often overlooking essential three-dimensional (3D) conformational information due to the inherent limitations of 2D structures in conveying atomic spatial relationships. In this study, we propose employing the Gram matrix as a condensed representation of 3D molecular structures and for efficient pretraining objectives. Subsequently, we leverage this matrix to construct a novel molecular representation model, Pre-GTM, which inherently encapsulates 3D information. The model accurately predicts the 3D structure of a molecule by estimating the Gram matrix. Our findings demonstrate that Pre-GTM model outperforms the baseline Graphormer model and other pretrained models in the QM9 and MoleculeNet quantitative property prediction task. The integration of the Gram matrix as a condensed representation of 3D molecular structure, incorporated into the Pre-GTM model, opens up promising avenues for its potential application across various domains of molecular research, including drug design, materials science, and chemical engineering.

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

Cited by 9 publications

References 46 publications

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention

Accurate prediction of drug combination risk levels based on relational graph convolutional network and multi-head attention

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug–drug interaction prediction

Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining

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