ArticleFrontiers in bioinformatics2022
The druggable genome: Twenty years later.
Article in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Special Issue "Heterocyclic Compounds: Synthesis, Design, and Biological Activity".International journal of molecular sciences · 2026Article
- Bioinformatics and experimental validation of druggable targets in non-alcoholic fatty liver disease.Scientific reports · 2026Article
- Pharmacotherapy Risks in Rare Genetic Diseases: Cross-Referencing ACMG Secondary Findings v3.2 List With Clinical Databases.Clinical and translational science · 2026Article
- Ultra-sensitive metaproteomics redefines the dark metaproteome, uncovering host-microbiome interactions and drug targets in intestinal diseases.Nature communications · 2025Article
- Article
- Affordable mRNA Novel Proteins, Recombinant Protein Conversions, and Biosimilars-Advice to Developers and Regulatory Agencies.Biomedicines · 2025Review
- Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics.Chemical reviews · 2024Review
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- Remapping the Chemical Space and the Pharmacological Space of Drugs: What Can We Expect from the Road Ahead?Pharmaceuticals (Basel, Switzerland) · 2024Article
- Strategies for translating proteomics discoveries into drug discovery for dementia.Neural regeneration research · 2024Review
- Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.Archives of pharmacal research · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target's druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.
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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.