Drug-drug Similarity Network Research Articles

Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug-Gene Interactions.

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list.

HNet-DNN: Inferring New Drug-Disease Associations with Deep Neural Network Based on Heterogeneous Network Features.

Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, referred to as drug repositioning, which provides an economical and efficient way for drug discovery. With increasing volumes of large-scale chemical, genomic, and pharmacological data sets generated by the high-throughput technique, it is crucial to develop systematic and rational computational approaches to identify new indications of approved drugs. In this paper, we introduce HNet-DNN, which utilizes a deep neural network (DNN), to predict new drug-disease associations based on the features extracted from the drug-disease heterogeneous network. Instead of the straightforward concatenation of chemical and phenotypic features as the input of DNN, we used these raw features of drugs and diseases to construct a drug-drug similarity network and a disease-disease similarity network, and then built a drug-disease heterogeneous network by integrating known drug-disease associations. Subsequently, we extracted topological features for drug-disease associations from the heterogeneous network and used them to train a DNN model. Our intensive performance evaluations demonstrated that HNet-DNN effectively exploits the features of the heterogeneous network to boost the predictive performance of drug-disease associations. Compared with a couple of typical classifiers and competitive approaches, our method not only achieved state-of-the-art performance but also effectively alleviated the overfitting problem. Moreover, we ran HNet-DNN to predict new drug-disease associations and carried out case studies to verify the effectiveness of our method.

Computational drug repositioning using meta-path-based semantic network analysis

BackgroundDrug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network. Therefore, it is urgent to develop a rational method to infer new indications for approved drugs.ResultsIn this study, we proposed a novel methodology named HeteSim_DrugDisease (HSDD) for the prediction of drug repositioning. Firstly, we build the drug-drug similarity network and disease-disease similarity network by integrating the information of drugs and diseases. Secondly, a drug-disease heterogeneous network is constructed, which combines the drug similarity network, disease similarity network as well as the known drug-disease association network. Finally, HSDD predicts novel drug-disease associations based on the HeteSim scores of different meta-paths. The experimental results show that HSDD performs significantly better than the existing state-of-the-art approaches. HSDD achieves an AUC score of 0.8994 in the leave-one-out cross validation experiment. Moreover, case studies for selected drugs further illustrate the practical usefulness of HSDD.ConclusionsHSDD can be an effective and feasible way to infer the associations between drugs and diseases using on meta-path-based semantic network analysis.

Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk.

Computational techniques for foreseeing drug-disease associations by means of incorporating gene expression as well as biological network give high intuitions to the composite associations amongst targets, drugs, disease genes in addition to the diseases at a system level. Hepatocellular Carcinoma (HCC) is a malevolent tumor containing a greater rate of sickness as well as mortality. In the present work, an Integrative framework is presented with the aim of resolving this problem, for identifying new Drugs for HCC dependent upon Multi-Source Random Walk (PD-MRW), in which score the complete drugs by means of building the drug-drug similarity network. On the other hand, the collection of clinical phenotypes as well as drug side effects in combination with patient-specific genetic info. As a result, the formation of disease-drug networks that denotes the prescriptions, which are allotted to treat those diseases that are not concentrated by means of PD-MRW model. With the aim of overcoming this issue, this research offers an integrative framework for foreseeing new drugs as well as diseases for HCC dependent upon Multi-Source Simulated Annealing based Random Walk (PDD-MSSARW). Primarily, build a Gene-Gene Weighted Interaction Network (GWIN), dependent upon the gene expression as well as protein interaction network. After that, construct a drug-drug similarity network, dependent upon multi-source random walk in GWIN, disease-drug similarity network with the help of Similarity Weighted Bipartite Graph Network (SWBGN) that is build up in which the nodes are drugs as well as association among one node to another node that explains the disease diagnoses. Lastly, dependent upon the known drugs for HCC, score the entire drugs in the similarity networks. The sturdiness of the likelihoods, their overlap with those stated in Comparative Toxicogenomics Database (CTD) as well as kinds of literature, and their enhanced KEGG pathway illustrate PDD-MSSARW method be capable of efficiently find out novel drug signs.

Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks.

BackgroundSince traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale.ResultsIn this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.ConclusionsWe proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.

Prediction of Novel Drugs for Hepatocellular Carcinoma Based on Multi-Source Random Walk.

Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to predict novel d rugs for HCC based on multi-source random walk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a gene-gene weighted i nteraction network (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank = 9 in top-20 list) is proven to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And, it has 11 overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.

Drug–target interaction prediction by random walk on the heterogeneous network

Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.