ArticleCommunications biology2022
DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Pooled it
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Genetically supported drug target prioritization for rare diseases.Genome medicine · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
- Genome-wide analysis of cardiac ventricular phenotypes reveals novel loci and therapeutic targets for heart failure.Nature communications · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Leveraging the shared and opposing genetic mechanisms in the heritable cardiomyopathies.Research square · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Identification of PANoptosis hub genes driving immune activation and tubulointerstitial injury in diabetic kidney disease by integrative bioinformatics and machine learning.Frontiers in immunology · 2026Article
- LOSTdb: a manually curated multi-omics database for lung cancer research.BMC bioinformatics · 2025Article
- Target and biomarker exploration portal for drug discovery.Bioinformatics (Oxford, England) · 2025Article
- Immune transcriptomic differences in paediatric patients with SARS-CoV-2 compared to other lower respiratory tract infections.bioRxiv : the preprint server for biology · 2025Article
- The proteogenomic landscape of the human kidney and implications for cardio-kidney-metabolic health.Nature medicine · 2025 · on this mapArticle
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Integrating in-silico and experimental validation approaches to unveil the therapeutic mechanism of naringenin against breast cancer.Scientific reports · 2025Article
- Development of a genetic priority score to predict drug side effects using human genetic evidence.Nature communications · 2025Article
- DrugProtAI: A machine learning-driven approach for predicting protein druggability through feature engineering and robust partition-based ensemble methods.Briefings in bioinformatics · 2025Article
- DrugTar improves druggability prediction by integrating large language models and gene ontologies.Bioinformatics (Oxford, England) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 4 institutions in 5 countries.
Funding
Abstract
The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human exome. DrugnomeAI integrates gene-level properties from 15 sources resulting in 324 features. The tool generates exome-wide predictions based on labelled sets of known drug targets (median AUC: 0.97), highlighting features from protein-protein interaction networks as top predictors. DrugnomeAI provides generic as well as specialised models stratified by disease type or drug therapeutic modality. The top-ranking DrugnomeAI genes were significantly enriched for genes previously selected for clinical development programs (p value < 1 × 10
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Registered trials
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.