ArticleDatabase : the journal of biological databases and curation2021
Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships.
Article in Database : the journal of biological databases and curation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Integrating Pharmacogenomics and Network Topology for Machine Learning Prediction of HLA-Associated Severe Cutaneous Adverse Drug Reactions.International journal of molecular sciences · 2026Article
- MSAT: a FAERS-informed heterogeneous graph neural network for pharmacovigilance prediction of Chinese materia medica-associated adverse drug reactions.Frontiers in pharmacology · 2026Article
- Phenotypic similarity of adverse drug reactions and disease phenotypes is a bridge to mechanistic discovery.npj drug discovery · 2025Article
- Advancing personalized cancer therapy: Onko_DrugCombScreen-a novel Shiny app for precision drug combination screening.NAR genomics and bioinformatics · 2025Article
- Large-scale prediction of adverse drug reactions-related proteins with network embedding.Bioinformatics (Oxford, England) · 2023Article
- Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning.Frontiers in bioinformatics · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs. Database URL: http://www.bioinsilico.org/T-ARDIS.
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