SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning

Wu, Zengrui; Cheng, Feixiong; Li, Jie; Li, Weihua; Liu, Guixia; Tang, Yun

doi:10.1093/bib/bbw012

Cited by 90 publications

(150 citation statements)

References 72 publications

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“…In order to increase the data quality of the computationally predicted DTIs, we only used the predicted targets ranked in top 5 candidates from the best network model (bSDTNBI_KR) described in our previous studies. 20, 21 In total, Exp&ComNet contains 1,623 known and 1,259 predicted DTIs (Supporting Information, Table S7) connecting 275 natural products and 525 targets.…”

Section: Resultsmentioning

confidence: 99%

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Fang

Cai

et al. 2017

J. Chem. Inf. Model.

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Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein and kaempherol) with high scores were validated by various literatures. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with the known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram and metformin) with new mechanism-of-action were validated by various published experimental evidences. In summary, this study offers powerful computational systems pharmacology approaches and tools for development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

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Section: Resultsmentioning

confidence: 99%

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Fang

Cai

et al. 2017

J. Chem. Inf. Model.

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“…Thirdly, the current SMiR-NBI model cannot predict new miRNAs for small molecules not interlinking with the existing SMiR-NBI network, such as benzothiazole and lovastatin here [25]. Recently, our group developed a SDTNBI algorithm [46] that can predict drug targets for new chemical entities. We plan to apply our SDTNBI algorithm for predicting potential miRNAs for such kinds of novel or isolated small molecules.…”

Section: Discussionmentioning

confidence: 99%

Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs

Lei

et al. 2016

Oncotarget

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As the recent development of high-throughput technologies in cancer pharmacogenomics, there is an urgent need to develop new computational approaches for comprehensive identification of new pharmacogenomic biomarkers, such as microRNAs (miRNAs). In this study, a network-based framework, namely the SMiR-NBI model, was developed to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs on the basis of a heterogeneous network connecting drugs, miRNAs and genes. A high area under the receiver operating characteristic curve of 0.820 ± 0.013 was yielded during 10-fold cross validation. In addition, high performance was further validated in identifying new anticancer mechanism-of-action for natural products and non-steroidal anti-inflammatory drugs. Finally, the newly predicted miRNAs for tamoxifen and metformin were experimentally validated in MCF-7 and MDA-MB-231 breast cancer cell lines via qRT-PCR assays. High success rates of 60% and 65% were yielded for tamoxifen and metformin, respectively. Specifically, 11 oncomiRNAs (e.g. miR-20a-5p, miR-27a-3p, miR-29a-3p, and miR-146a-5p) from the top 20 predicted miRNAs were experimentally verified as new pharmacogenomic biomarkers for metformin in MCF-7 or MDA-MB-231 cell lines. In summary, the SMiR-NBI model would provide a powerful tool to identify potential pharmacogenomic biomarkers characterized by miRNAs in the emerging field of precision cancer medicine, which is available at http://lmmd.ecust.edu.cn/database/smir-nbi/.

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“…Network-based inference approaches were wildly used in drug repositioning [20, 21]. Here we infer the potential drug repositioning mechanism through compound-target-disease network.…”

Section: Methodsmentioning

confidence: 99%

DrAFC: Drug Repositioning Through Anti-Fibrosis Characteristic

Gao

et al. 2020

Preprint

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16Fibrosis is a key component in the pathogenic mechanism of many diseases. These 17 diseases involving fibrosis may share common mechanisms, therapeutic targets and 18 therefore, common intervention strategies and medicines may be applicable for these 19 diseases. For this reason, deliberately introducing anti-fibrosis characteristics into 20 modelling may lead to more success in drug repositioning. In this study, anti-fibrosis 21 knowledge base was first built by collecting data from multiple resources. Both 22 structural and biological profiles were derived from the knowledge base and used for 23 constructing machine learning models including Structural Profile Prediction Model 24 (SPPM) and Biological Profile Prediction Model (BPPM). Three external public data 25 sets were employed for validation purpose and further exploration of potential 26 repositioning drugs in wider chemical space. The resulting SPPM and BPPM models 27 achieve area under the receiver operating characteristic curve (AUC) of 0.879 and 28 0.972 in the training set, and 0.814 and 0.874 in the testing set. Additionally, our 29 results also demonstrate that substantial amount of multi-targeting natural products 30 possess notable anti-fibrosis characteristics and might serve as encouraging candidates 31 in fibrosis treatment and drug repositioning. To leverage our methodology and 32 findings, we developed repositioning prediction platform, Drug Repositioning based 33 on Anti-Fibrosis Characteristic (Dr AFC) that is freely accessible via 34 https://www.biosino.org/drafc. 35 36

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SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug–target interactions and drug repositioning

Cited by 90 publications

References 72 publications

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs

DrAFC: Drug Repositioning Through Anti-Fibrosis Characteristic

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