SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

It is well known that drug discovery for complex diseases via biological experiments is a time-consuming and expensive process. Alternatively, the computational methods provide a low-cost and high-efficiency way for predicting drug-target interactions (DTIs) from biomolecular networks. However, the current computational methods mainly deal with DTI predictions of known drugs; there are few methods for large-scale prediction of failed drugs and new chemical entities that are currently stored in some biological databases may be effective for other diseases compared with their originally targeted diseases. In this study, we propose a method (called SDTRLS) which predicts DTIs through RLS-Kron model with chemical substructure similarity fusion and Gaussian Interaction Profile (GIP) kernels. SDTRLS can be an effective predictor for targets of old drugs, failed drugs, and new chemical entities from large-scale biomolecular network databases. Our computational experiments show that SDTRLS outperforms the state-of-the-art SDTNBI method; specifically, in the G protein-coupled receptors (GPCRs) external validation, the maximum and the average AUC values of SDTRLS are 0.842 and 0.826, respectively, which are superior to those of SDTNBI, which are 0.797 and 0.766, respectively. This study provides an important basis for new drug development and drug repositioning based on biomolecular networks.

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Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

Journal of Translational Medicine ◽

10.1186/s12967-020-02490-x ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Bo-Ya Ji ◽

Zhu-Hong You ◽

Han-Jing Jiang ◽

Zhen-Hao Guo ◽

Kai Zheng

Keyword(s):

Computational Methods ◽

Information Integration ◽

Drug Target ◽

Large Scale ◽

New Drugs ◽

Single Type ◽

Information Network ◽

Potential Drug Target ◽

Potential Drug ◽

Network Embedding

Abstract Background The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. Methods In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous multi-molecuar information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. Results In the results, under the five-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. Conclusions In short, these results indicate that our method can be a powerful tool for predicting potential drug-target interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.

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A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information

10.1101/100305 ◽

2017 ◽

Cited By ~ 4

Author(s):

Yunan Luo ◽

Xinbin Zhao ◽

Jingtian Zhou ◽

Jinglin Yang ◽

Yanqing Zhang ◽

...

Keyword(s):

Heterogeneous Network ◽

Drug Target ◽

Large Scale ◽

Molecular Mechanisms ◽

Inflammatory Diseases ◽

Drug Repositioning ◽

Heterogeneous Data ◽

Heterogeneous Information ◽

Cox Inhibitors ◽

New Drug

AbstractThe emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. Systematic integration of these heterogeneous data not only serves as a promising tool for identifying new drug-target interactions (DTIs), which is an important step in drug development, but also provides a more complete understanding of the molecular mechanisms of drug action. In this work, we integrate diverse drug-related information, including drugs, proteins, diseases and side-effects, together with their interactions, associations or similarities, to construct a heterogeneous network with 12,015 nodes and 1,895,445 edges. We then develop a new computational pipeline, called DTINet, to predict novel drug-target interactions from the constructed heterogeneous network. Specifically, DTINet focuses on learning a low-dimensional vector representation of features for each node, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then predicts the likelihood of a new DTI based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for DTI prediction. Moreover, we have experimentally validated the novel interactions between three drugs and the cyclooxygenase (COX) protein family predicted by DTINet, and demonstrated the new potential applications of these identified COX inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs. The source code of DTINet and the input heterogeneous network data can be downloaded from http://github.com/luoyunan/DTINet.

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Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Pharmaceutics ◽

10.3390/pharmaceutics11080377 ◽

2019 ◽

Vol 11 (8) ◽

pp. 377 ◽

Cited By ~ 6

Author(s):

Hanbi Lee ◽

Wankyu Kim

Keyword(s):

Neural Network ◽

Drug Target ◽

Large Scale ◽

Deep Neural Network ◽

Structural Information ◽

Drug Repositioning ◽

Expression Profiles ◽

Ppi Network ◽

Drug Induced ◽

Integrated Network

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

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ID:2047 Drug respositioning by integrating known disease-gene and drug-target associations

Biomedical Research and Therapy ◽

10.15419/bmrat.v4is.281 ◽

2017 ◽

Vol 4 (S) ◽

pp. 76

Author(s):

Duc-Hau Le ◽

Duc-Hau Le

Keyword(s):

Computational Methods ◽

Drug Target ◽

Disease Gene ◽

State Of The Art ◽

Drug Repositioning ◽

Least Square ◽

Data Sources ◽

Chemical Structures ◽

Drug Compounds ◽

Disease Associations

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs, diseases and different approaches. Depending on where the discovery of drug-disease relationships comes from, proposed computational methods can be categorized as either ‘drug-based’ or ‘disease-based’. The proposed methods are usually based on an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarity between drugs and between diseases is usually used as inputs. In addition, known drug-disease associations are also needed for the methods. It should be noted that these associations are still not well established due to many of marketed drugs have been withdrawn and this could affect to outcome of the methods. In this study, instead of using the known drug-disease associations, we based on known disease-gene and drug-target associations. In addition, similarity between drugs measured by chemical structures of drug compounds and similarity between diseases sharing phenotypes are used. Then, a semi-supervised learning model, Regularized Least Square (RLS), which can exploit these information effectively, is used to find new uses of drugs. Experiment results demonstrate that our method, namely RLSDR, outperforms several state-of-the-art existing methods in terms of area under the ROC curve (AUC). Novel indications for a number of drugs are identified and validated by evidences from different resources

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Prediction of Drug-target interactions from heterogeneous information network based on LINE embedding model

10.21203/rs.3.rs-16492/v2 ◽

2020 ◽

Author(s):

Bo-Ya Ji ◽

Zhu-Hong You ◽

Han-Jing Jiang ◽

Zhen-Hao Guo ◽

Kai Zheng

Keyword(s):

Computational Methods ◽

Drug Target ◽

Large Scale ◽

New Drugs ◽

Single Type ◽

Information Network ◽

Potential Drug ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information

Abstract Background: The prediction of potential drug-protein target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. Methods: In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. Results: In the results, under the 5-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. Conclusions: In short, these results indicate that our method can be a powerful tool for predicting potential drug-protein interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.

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Network-based prediction of drug–target interactions using an arbitrary-order proximity embedded deep forest

Bioinformatics ◽

10.1093/bioinformatics/btaa010 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2805-2812 ◽

Cited By ~ 16

Author(s):

Xiangxiang Zeng ◽

Siyi Zhu ◽

Yuan Hou ◽

Pengyue Zhang ◽

Lang Li ◽

...

Keyword(s):

Biological Networks ◽

Drug Target ◽

Large Scale ◽

Arbitrary Order ◽

Characteristic Curve ◽

External Validation ◽

Molecular Targets ◽

Drug Repurposing ◽

Supplementary Information ◽

Deep Forest

Abstract Motivation Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug–target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. Results In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF. Supplementary information Supplementary data are available at Bioinformatics online.

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deepDR: a network-based deep learning approach to in silico drug repositioning

Bioinformatics ◽

10.1093/bioinformatics/btz418 ◽

2019 ◽

Vol 35 (24) ◽

pp. 5191-5198 ◽

Cited By ~ 76

Author(s):

Xiangxiang Zeng ◽

Siyi Zhu ◽

Xiangrong Liu ◽

Yadi Zhou ◽

Ruth Nussinov ◽

...

Keyword(s):

Deep Learning ◽

In Silico ◽

Large Scale ◽

High Efficiency ◽

Drug Repositioning ◽

Rapid Development ◽

Characteristic Curve ◽

Supplementary Information ◽

Learning Approach ◽

Approved Drugs

Abstract Motivation Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. Results In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug–disease, one drug-side-effect, one drug–target and seven drug–drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug–disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug–disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer’s disease (e.g. risperidone and aripiprazole) and Parkinson’s disease (e.g. methylphenidate and pergolide). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR Supplementary information Supplementary data are available online at Bioinformatics.

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DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning

Journal of Cheminformatics ◽

10.1186/s13321-021-00552-w ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Maha A. Thafar ◽

Rawan S. Olayan ◽

Somayah Albaradei ◽

Vladimir B. Bajic ◽

Takashi Gojobori ◽

...

Keyword(s):

Feature Extraction ◽

Ensemble Learning ◽

Link Prediction ◽

Drug Target ◽

Prediction Accuracy ◽

Large Scale ◽

Drug Repositioning ◽

Representation Learning ◽

Target Interaction ◽

Learning Techniques

AbstractDrug–target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug–target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

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Relation Extraction between Biomedical Entities from Literature using Semi- Supervised Learning Approach

10.5121/csit.2021.112306 ◽

2021 ◽

Author(s):

Saranya M ◽

Arockia Xavier Annie R ◽

Geetha T V

Keyword(s):

Drug Target ◽

Large Scale ◽

Drug Repositioning ◽

Relation Extraction ◽

Biomedical Literature ◽

Learning Approach ◽

Computational Approaches ◽

The World ◽

The Cost ◽

Very High

Now-a-days, people around the world are infected by many new diseases. The cost of developing or discovering a new drug for the newly discovered disease is very high and prolonged process. These could be eliminated with the help of already existing resources. To identify the candidates from the existing drugs, we need to extract the relation between the drug, target and disease by textming a large-scale literature. Recently, computational approaches which is used for identifying the relationships between the entities in biomedical domain are appearing as an active area of research for drug discovery as it needs more man power. Due to the limited computational approaches, the relation extraction between drug-gene and genedisease association from the unstructured biomedical documents is very hard. In this work, we proposed a semi-supervised approach named pattern based bootstrapping method to extract the direct relations between drug, gene and disease from the biomedical literature. These direct relationships are used to infer indirect relationships between entities such as drug and disease. Now these indirect relationships are used to determine the new candidates for drug repositioning which in turn will reduce the time and the patient’s risk.

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NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

10.1101/261396 ◽

2018 ◽

Cited By ~ 1

Author(s):

Fangping Wan ◽

Lixiang Hong ◽

An Xiao ◽

Tao Jiang ◽

Jianyang Zeng

Keyword(s):

Drug Development ◽

Biological Networks ◽

Heterogeneous Network ◽

Drug Target ◽

Large Scale ◽

Drug Repositioning ◽

Supplementary Information ◽

Functional Roles ◽

Aggregation Techniques ◽

Wide Range

AbstractMotivationAccurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.ResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.Availability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/[email protected] informationSupplementary data are available at Bioinformatics online.

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