A landscape for drug-target interactions based on network analysis

In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.

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Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

Alzheimer s Research & Therapy ◽

10.1186/s13195-021-00826-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shingo Tsuji ◽

Takeshi Hase ◽

Ayako Yachie-Kinoshita ◽

Taiko Nishino ◽

Samik Ghosh ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Drug Target ◽

Target Genes ◽

Drug Repositioning ◽

Therapeutic Targets ◽

Machine Learning Techniques ◽

Computational Framework ◽

Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

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Drug-Target Interaction Network Analysis of Gene-Phenotype Connectivity Maintained by Genistein

Journal of Computational Biology ◽

10.1089/cmb.2019.0443 ◽

2020 ◽

Vol 27 (12) ◽

pp. 1678-1687 ◽

Cited By ~ 1

Author(s):

Baoshan Li ◽

Yi Jiang ◽

Jingxin Chu ◽

Qian Zhou

Keyword(s):

Network Analysis ◽

Drug Target ◽

Interaction Network ◽

Target Interaction

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DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

Journal of Translational Medicine ◽

10.1186/s12967-020-02602-7 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Yihan Zhao ◽

Kai Zheng ◽

Baoyi Guan ◽

Mengmeng Guo ◽

Lei Song ◽

...

Keyword(s):

Neural Networks ◽

Molecular Mechanism ◽

Drug Target ◽

Drug Repositioning ◽

Biological Data ◽

Mapping Space ◽

Target Interaction ◽

Network Representation ◽

Combination Methods ◽

Low Dimensional

Abstract Background Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. Methods A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo. Results The experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models. Conclusions DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

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Drug target gene-based analyses of drug repositionability in rare and intractable diseases

Scientific Reports ◽

10.1038/s41598-021-91428-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ryuichi Sakate ◽

Tomonori Kimura

Keyword(s):

Drug Development ◽

Drug Target ◽

Target Gene ◽

Target Genes ◽

Drug Information ◽

Drug Repositioning ◽

Clinical Trial Data ◽

Insufficient Information ◽

Disease Pair ◽

Low Prevalence

AbstractDrug development for rare and intractable diseases has been challenging for decades due to the low prevalence and insufficient information on these diseases. Drug repositioning is increasingly being used as a promising option in drug development. We aimed to analyze the trend of drug repositioning and inter-disease drug repositionability among rare and intractable diseases. We created a list of rare and intractable diseases based on the designated diseases in Japan. Drug information extracted from clinical trial data were integrated with information of drug target genes, which represent the mechanism of drug action. We obtained 753 drugs and 551 drug target genes from 8307 clinical trials for 189 diseases or disease groups. Trend analysis of drug sharing between a disease pair revealed that 1676 drug repositioning events occurred in 4401 disease pairs. A score, Rgene, was invented to investigate the proportion of drug target genes shared between a disease pair. Annual changes of Rgene corresponded to the trend of drug repositioning and predicted drug repositioning events occurring within a year or two. Drug target gene-based analyses well visualized the drug repositioning landscape. This approach facilitates drug development for rare and intractable diseases.

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The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective

PLoS ONE ◽

10.1371/journal.pone.0165737 ◽

2016 ◽

Vol 11 (11) ◽

pp. e0165737 ◽

Cited By ~ 30

Author(s):

Ying Hong Li ◽

Pan Pan Wang ◽

Xiao Xu Li ◽

Chun Yan Yu ◽

Hong Yang ◽

...

Keyword(s):

Comparative Study ◽

Drug Target ◽

Interaction Network ◽

Target Interaction ◽

Human Kinome

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DLDTI: A learning-based framework for drug-target interaction identification using neural networks and network representation

10.21203/rs.3.rs-56700/v3 ◽

2020 ◽

Author(s):

Yihan Zhao ◽

Kai Zheng ◽

Baoyi Guan ◽

Mengmeng Guo ◽

Lei Song ◽

...

Keyword(s):

Neural Networks ◽

Molecular Mechanism ◽

Drug Target ◽

Drug Repositioning ◽

Biological Data ◽

Mapping Space ◽

Target Interaction ◽

Network Representation ◽

Combination Methods ◽

Low Dimensional

Abstract Background: Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.Methods: A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI， was generated. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validate the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo.Results: The experimental results show that the DLDTI model achieves promising performance under 5-fold cross-validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models.Conclusions: DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

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DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

PLoS ONE ◽

10.1371/journal.pone.0246920 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246920

Author(s):

Sk Mazharul Islam ◽

Sk Md Mosaddek Hossain ◽

Sumanta Ray

Keyword(s):

Drug Discovery ◽

Drug Target ◽

Drug Repositioning ◽

Geometric Mean ◽

Imbalanced Data ◽

Drug Repurposing ◽

Search Space ◽

Classification Model ◽

Target Interaction ◽

Rough Approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

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Drug-Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking

BioMed Research International ◽

10.1155/2021/6690154 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yihua Ye ◽

Yuqi Wen ◽

Zhongnan Zhang ◽

Song He ◽

Xiaochen Bo

Keyword(s):

Partial Order ◽

Matrix Factorization ◽

Drug Target ◽

Drug Repositioning ◽

Expression Profiles ◽

Learning To Rank ◽

Prediction Performance ◽

Target Interaction ◽

Bayesian Personalized Ranking ◽

Personalized Ranking

The prediction of drug-target interaction (DTI) is a key step in drug repositioning. In recent years, many studies have tried to use matrix factorization to predict DTI, but they only use known DTIs and ignore the features of drug and target expression profiles, resulting in limited prediction performance. In this study, we propose a new DTI prediction model named AdvB-DTI. Within this model, the features of drug and target expression profiles are associated with Adversarial Bayesian Personalized Ranking through matrix factorization. Firstly, according to the known drug-target relationships, a set of ternary partial order relationships is generated. Next, these partial order relationships are used to train the latent factor matrix of drugs and targets using the Adversarial Bayesian Personalized Ranking method, and the matrix factorization is improved by the features of drug and target expression profiles. Finally, the scores of drug-target pairs are achieved by the inner product of latent factors, and the DTI prediction is performed based on the score ranking. The proposed model effectively takes advantage of the idea of learning to rank to overcome the problem of data sparsity, and perturbation factors are introduced to make the model more robust. Experimental results show that our model could achieve a better DTI prediction performance.

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