Target identification among known drugs by deep learning from heterogeneous networks

Zeng, Xiangxiang; Zhu, Siyi; Lü, Weiqiang; Liu, Zehui; Huang, Jin; Zhou, Yadi; Fang, Jiansong; Huang, Yin; Guo, Huimin; Li, Lang; Trapp, Bruce D.; Nussinov, Ruth; Eng, Charis; Loscalzo, Joseph; Cheng, Feixiong

doi:10.1039/c9sc04336e

Cited by 249 publications

(183 citation statements)

References 120 publications

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“…After adding its target, an RNA helicase enzyme EIF4A 76 , silvestrol was predicted to be significantly associated with HCoVs (Z = -1.24, P = 0.041) by network proximity analysis. To increase coverage of drug-target networks, we may use computational approaches to systematically predict the drug-target interactions further 25,26 . In addition, the collected virus-host interactions are far from completeness and the quality can be influenced by multiple factors, including different experimental assays and human cell line models.…”

Section: Discussionmentioning

confidence: 99%

“…This methodology allows to identify several candidate repurposable drugs for Ebola virus 11,14 . Our work over the last decade has demonstrated how network strategies can, for example, be used to identify effective repurposable drugs 13,[22][23][24][25][26][27] and drug combinations 28 for multiple human diseases. For example, network-based drug-disease proximity sheds light on the relationship between drugs (e.g., drug targets) and disease modules (molecular determinants in disease pathobiology modules within the PPIs), and can serve as a useful tool for efficient screening of potentially new indications for approved drugs, as well as drug combinations, as demonstrated in our recent studies 13,23,27,28 .…”

Section: Introductionmentioning

confidence: 99%

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Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

et al. 2020

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Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

et al. 2020

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“…Yet, without foreknowledge of the complete drug-target network, development of promising and affordable approaches for effective treatment of complex diseases is challenging. 10 Because drug targets do not operate in isolation from the complex system of proteins that comprise the molecular machinery of the cells with which they associate, each drug-target interaction (panel) should be examined in an integrative context (figure 2). 11 Therapeutic interventions need to consider the perturbation of disease system properties (termed network medicine [panel]), and have little to do, functionally speaking, with genetic and genomic events alone.…”

Section: Emerging Challenges and Opportunities In Drug Discoverymentioning

confidence: 99%

Artificial intelligence in COVID-19 drug repurposing

Zhou

Wang

Tang

et al. 2020

The Lancet Digital Health

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Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

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“…In vitro experimental evidence also validated the predicted targets of this known drug [9]. Further, combined with in silico prediction, in vitro validation and animal phenotype model demonstrated that, topotecan, a topoisomerase inhibitor also had the potential to act as a direct inhibitor of human retinoic-acidreceptor-related orphan receptor-gamma t (ROR-γt) [10].…”

Section: Introductionmentioning

confidence: 75%

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

Zhao

Zheng

Guan

et al. 2020

Preprint

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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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Target identification among known drugs by deep learning from heterogeneous networks

Abstract: 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.

Cited by 249 publications

References 120 publications

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

Artificial intelligence in COVID-19 drug repurposing

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

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