GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

Cheng, Shicheng; Zhang, Liang; Jin, Bo; Zhang, Qiang; Lu, Xinjiang; You, Ming; Tian, Xueqing

doi:10.3390/app11073239

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Additionally, MolCLR [126] and CKGNN [127] learn molecular representations with graph-level contrast-based pretext tasks. Besides, GraphMS [128] and MIRACLE [129] employ contrastive learning to solve the drug-target and drug-drug interaction prediction problems.…”

Section: Practical Applicationsmentioning

confidence: 99%

Graph Self-Supervised Learning: A Survey

Liu

Jin

Pan

et al. 2022

IEEE Trans. Knowl. Data Eng.

171

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Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including heavy label reliance, poor generalization, and weak robustness. To address these issues, self-supervised learning (SSL), which extracts informative knowledge through well-designed pretext tasks without relying on manual labels, has become a promising and trending learning paradigm for graph data. Different from SSL on other domains like computer vision and natural language processing, SSL on graphs has an exclusive background, design ideas, and taxonomies. Under the umbrella of graph self-supervised learning, we present a timely and comprehensive review of the existing approaches which employ SSL techniques for graph data. We construct a unified framework that mathematically formalizes the paradigm of graph SSL. According to the objectives of pretext tasks, we divide these approaches into four categories: generation-based, auxiliary property-based, contrast-based, and hybrid approaches. We further describe the applications of graph SSL across various research fields and summarize the commonly used datasets, evaluation benchmark, performance comparison and open-source codes of graph SSL. Finally, we discuss the remaining challenges and potential future directions in this research field.

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Section: Practical Applicationsmentioning

confidence: 99%

Graph Self-Supervised Learning: A Survey

Liu

Jin

Pan

et al. 2022

IEEE Trans. Knowl. Data Eng.

171

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show abstract

“…Cheng et al [ 33 ] came up with the GraphMS model, which is an end-to-end network model made just for figuring out DTIs using low-level representations. One important thing about it is that it puts a lot of weight on node-level representation accountability.…”

Section: Related Workmentioning

confidence: 99%

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

Djeddi,

Hermi,

Ben Yahia

et al. 2023

BMC Bioinformatics

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Background The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery. Results The present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target–target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets. Conclusions The effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.

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“…Many computing methods were used for effective drug target prediction. To better find potential drug targets and provide new options for drug redirection, Cheng et al ( Cheng et al, 2021 ) established the GraphMS model. They fused heterogeneous graph information using mutual information in the heterogeneous graph to obtain effective node information and substructure information.…”

Section: Introductionmentioning

confidence: 99%

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

et al. 2021

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Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

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GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

Cited by 6 publications

References 28 publications

Graph Self-Supervised Learning: A Survey

Graph Self-Supervised Learning: A Survey

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

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