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.

Target identification and drug repurposing could benefit from network-based, rational deep learning prediction, and explore the relationship between drugs and targets in the heterogeneous drug–gene–disease network.

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.

No abstract

Heterogeneous information networks (HINs) currently play an important role in daily life. HINs are applied in many fields, such as science research, e-commerce, recommendation systems, and bioinformatics. Particularly, HINs have been used in biomedical research. Algorithms have been proposed to calculate the correlations between drugs and targets and between diseases and genes. Recently, the interaction between drugs and human genes has become an important subject in the research on drug efficacy and human genomics. In previous studies, numerous prediction methods using machine learning and statistical prediction models were proposed to explore this interaction on the biological network. In the current work, we introduce a representation learning method into the biological heterogeneous network and use the representation learning models metapath2vec and metapath2vec++ on our dataset. We combine the adverse drug reaction (ADR) data in the drug–gene network with causal relationship between drugs and ADRs. This article first presents an analysis of the importance of predicting drug–gene relationships and discusses the existing prediction methods. Second, the skip-gram model commonly used in representation learning for natural language processing tasks is explained. Third, the metapath2vec and metapath2vec++ models for the example of drug–gene-ADR network are described. Next, the kernelized Bayesian matrix factorization algorithm is used to complete the prediction. Finally, the experimental results of both models are compared with Katz, CATAPULT, and matrix factorization, the prediction visualized using the receiver operating characteristic curves are presented, and the area under the receiver operating characteristic values for three varying algorithm parameters are calculated.

No abstract

No abstract

No abstract