ArticlePloS one2010
Network-based relating pharmacological and genomic spaces for drug target identification.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 1 of them a synthesis that pooled it.
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Who cites it
110 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Drug repositioning discovery for early- and late-stage non-small-cell lung cancer.BioMed research international · 2014Pooled it
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- Integrating network pharmacology and multi-omics in a systems approach: a mechanism study of Jinhong tablets against chronic superficial gastritis.Chinese medicine · 2025Article
- A computational-based new treatment strategy with three-armed RCT on Mycoplasma pneumoniae pneumonia in children.Chinese medicine · 2025Article
- Drug Repurposing to Inhibit Oncostatin M in Crohn's Disease.Molecules (Basel, Switzerland) · 2025Article
- Network Pharmacology and Molecular Dynamics Identified Potential Androgen Receptor-Targeted Metabolites inInternational journal of molecular sciences · 2025Article
- Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.Journal of pharmaceutical analysis · 2025Review
- Network pharmacology: a crucial approach in traditional Chinese medicine research.Chinese medicine · 2025Review
- Comprehensive dataset of heterogeneous network structures in traditional chinese medicine research.Data in brief · 2024Article
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- Uncovering the role of traditional Chinese medicine in immune-metabolic balance of gastritis from the perspective of Cold and Hot: Jin Hong Tablets as a case study.Chinese medicine · 2024Article
- Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology.Chinese herbal medicines · 2024Review
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- Dual-channel hypergraph convolutional network for predicting herb-disease associations.Briefings in bioinformatics · 2024Article
- Quantitative Proteomics Combined with Network Pharmacology Analysis Unveils the Biological Basis of Schisandrin B in Treating Diabetic Nephropathy.Combinatorial chemistry & high throughput screening · 2024Article
- Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.Briefings in bioinformatics · 2023Review
50 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying drug targets is a critical step in pharmacology. Drug phenotypic and chemical indexes are two important indicators in this field. However, in previous studies, the indexes were always isolated and the candidate proteins were often limited to a small subset of the human genome. METHODOLOGY/PRINCIPAL
findingsBased on the correlations observed in pharmacological and genomic spaces, we develop a computational framework, drugCIPHER, to infer drug-target interactions in a genome-wide scale. Three linear regression models are proposed, which respectively relate drug therapeutic similarity, chemical similarity and their combination to the relevance of the targets on the basis of a protein-protein interaction network. Typically, the model integrating both drug therapeutic similarity and chemical similarity, drugCIPHER-MS, achieved an area under the Receiver Operating Characteristic (ROC) curve of 0.988 in the training set and 0.935 in the test set. Based on drugCIPHER-MS, a genome-wide map of drug biological fingerprints for 726 drugs is constructed, within which unexpected drug-drug relations emerged in 501 cases, implying possible novel applications or side effects. CONCLUSIONS/SIGNIFICANCE: Our findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.
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