Graph convolutional networks for computational drug development and discovery

Sun, Mengying; Zhao, Sendong; Gilvary, Coryandar; Elemento, Olivier; Zhou, Jiayu; Wang, Fei

doi:10.1093/bib/bbz042

Cited by 285 publications

(179 citation statements)

References 63 publications

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“…GCNs have been used in computational drug discovery [34], including quantitative structure activity/property relationship prediction, interaction prediction, synthesis prediction, and de novo molecular design. The problem we explore in this paper, prediction of drug-target binding affinity, belongs to the task of interaction prediction, where the interactions could be among drugs, among proteins, or between drugs and proteins.…”

Section: Related Work 21 Drug Representationmentioning

confidence: 99%

“…Compared to the spectral-based methods which handle the whole graph simultaneously, the spatial approaches can instead process graph nodes in batches thus can be scalable to large graphs. Recent works on this approach include [21,23,12,37,36,40] GCNs have been used in computational drug discovery [34], including quantitative structure activity/property relationship prediction, interaction prediction, synthesis prediction, and de novo molecular design. The problem we explore in this paper, prediction of drug-target binding affinity, belongs to the task of interaction prediction, where the interactions could be among drugs, among proteins, or between drugs and proteins.…”

Section: Related Work 21 Drug Representationmentioning

confidence: 99%

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GraphDTA: Predicting drug–target binding affinity with graph neural networks

Nguyen

Quinn

et al. 2019

Preprint

146

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Background: While the development of new drugs is costly, time consuming, and often accompanied with safety issues, drug repurposing, where old drugs with established safety are used for medical conditions other than originally developed, is an attractive alternative. Then, how the old drugs work on new targets becomes a crucial part of drug repurposing and gains much of interest. Several statistical and machine learning models have been proposed to estimate drug-target binding affinity and deep learning approaches have been shown to be among state-of-the-art methods. However, drugs and targets in these models have been commonly represented in 1D strings, regardless the fact that molecules are by nature formed by the chemical bonding of atoms. Method: In this work, we propose GraphDTA to capture the structural information of drugs, possibly enhancing the predictive power of the affinity. In particular, unlike competing methods, drugs are represented as graphs and graph convolutional networks are used to learn drug-target binding affinity. We trial our method on two benchmark datasets of drug-target binding affinity and compare the performance with state-of-the-art models in the research field. Results: The results show that our proposed method can not only predict the affinity better than non-deep learning models, but also outperform competing deep learning approaches. This demonstrates the practical advantages of graph-based representation for molecules in providing accurate prediction of drug-target binding affinity. The application may also include any recommendation systems where either or both of the user-and product-like sides can be represented in graphs. Availability and implementation: The proposed models are implemented in Python. Related data, pre-trained models, and source code are publicly available at https://github.com/thinng/GraphDTA.

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Section: Related Work 21 Drug Representationmentioning

confidence: 99%

Section: Related Work 21 Drug Representationmentioning

confidence: 99%

GraphDTA: Predicting drug–target binding affinity with graph neural networks

Nguyen

Quinn

et al. 2019

Preprint

146

250

View full text Add to dashboard Cite

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“…The dense vector embedding, also called low-dimensional representations, are learned to preserve the structural relationships between nodes (e.g., drugs) of the network, and thus can be used as features in building machine learning models for various downstream tasks, such as link prediction [17]. Recently, the GCN has been applied to the field of drug development and discovery [20], such as molecular activity prediction [21], drug side effect prediction [22], drug target interactions prediction [23].…”

Section: Introductionmentioning

confidence: 99%

DPDDI: a deep predictor for drug-drug interactions

2020

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Background The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases. Results In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs. Conclusion We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.

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“…More recently, the emergence of deep learning (DL) methods has revolutionized this traditional cheminformatics task due to their extraordinary capacity to learn intricate relationships between structures and properties. [17][18][19][20][21][22][23][24] The models developed by DL can be roughly classified into two categories: descriptor-based models and graph-based models. [25] As to descriptor-based DL models, molecular descriptors and/or fingerprints commonly used in traditional quantitative structure-activity relationship (QSAR) models are used as the input, and then a specific DL architecture is employed to train a model.…”

Section: For Table Of Contents Use Only Introductionmentioning

confidence: 99%

“…Then, the learned atom representations can be used for the prediction of molecular properties through the read-out phase. [20,27] The key feature of GNN is its capacity to automatically learn task-specific representations using graph convolutions while does not need traditional hand-crafted descriptors and/or fingerprints. The state-of-the-art accuracy of GNN models in property prediction has been well represented.…”

Section: For Table Of Contents Use Only Introductionmentioning

confidence: 99%

Could Graph Neural Networks Learn Better Molecular Representation for Drug Discovery? A Comparison Study of Descriptor-based and Graph-based Models

Jiang

Hsieh

et al. 2020

Preprint

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Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves the best predictions for the regression tasks. Both RF and XGBoost can achieve reliable predictions for the classification tasks, and some of the graph-based models, such as Attentive FP and GCN, can yield outstanding performance for a fraction of larger or multi-task datasets. In terms of computational cost, XGBoost and RF are the two most efficient algorithms and only need a few seconds to train a model even for a large dataset. The model interpretations by the SHAP method can effectively explore the established domain knowledge for the descriptor-based models. Finally, we explored use of these models for virtual screening (VS) towards HIV and demonstrated that different ML algorithms offer diverse VS profiles. All in all, we believe that the off-the-shelf descriptor-based models still can be directly employed to accurately predict various chemical endpoints with excellent computability and interpretability.

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Graph convolutional networks for computational drug development and discovery

Cited by 285 publications

References 63 publications

GraphDTA: Predicting drug–target binding affinity with graph neural networks

GraphDTA: Predicting drug–target binding affinity with graph neural networks

DPDDI: a deep predictor for drug-drug interactions

Could Graph Neural Networks Learn Better Molecular Representation for Drug Discovery? A Comparison Study of Descriptor-based and Graph-based Models

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