ArticleBMC bioinformatics2014
Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- The future of metronomic chemotherapy: experimental and computational approaches of drug repurposing.Pharmacological reports : PR · 2025Review
- Systematic computational strategies for identifying protein targets and lead discovery.RSC medicinal chemistry · 2024Review
- A Network-Guided Approach to Discover Phytochemical-Based Anticancer Therapy: Targeting MARK4 for Hepatocellular Carcinoma.Frontiers in oncology · 2022Article
- Review
- Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma.JMIR medical informatics · 2020Article
- Article
- Revealing Drug-Target Interactions with Computational Models and Algorithms.Molecules (Basel, Switzerland) · 2019Review
- CSgator: an integrated web platform for compound set analysis.Journal of cheminformatics · 2019Article
- Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.BMC bioinformatics · 2019Article
- Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins.Frontiers in genetics · 2019Article
- Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences.Briefings in bioinformatics · 2018Review
- Carboplatin-induced hematotoxicity among patients with non-small cell lung cancer: Analysis on clinical adverse events and drug-gene interactions.Oncotarget · 2017Article
- High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI).BMC systems biology · 2016Article
- Proteome-wide drug screening using mass spectrometric imaging of bead-arrays.Scientific reports · 2016Article
- Building a virtual ligand screening pipeline using free software: a survey.Briefings in bioinformatics · 2016Review
- DASPfind: new efficient method to predict drug-target interactions.Journal of cheminformatics · 2016Article
- A Systems Approach Implicates a Brain Mitochondrial Oxidative Homeostasis Co-expression Network in Genetic Vulnerability to Alcohol Withdrawal.Frontiers in genetics · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInformation about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. DESCRIPTION: We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface.
conclusionsDrug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.