ArticlePloS one2020
TargetDB: A target information aggregation tool and tractability predictor.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
10 citing papers in PubMed.
- Integrating AI into next-generation PROTAC Engineering: a comprehensive toolkit for rational PROTAC design.Molecular diversity · 2026Review
- Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation.Nature machine intelligence · 2026Article
- A review on computational models for predicting protein solubility.Journal of microbiology (Seoul, Korea) · 2025Review
- Genome-wide association neural networks identify genes linked to family history of Alzheimer's disease.Briefings in bioinformatics · 2024Article
- Amyotrophic lateral sclerosis: a neurodegenerative disorder poised for successful therapeutic translation.Nature reviews. Drug discovery · 2023Review
- Article
- Application of artificial intelligence in the development ofFrontiers in artificial intelligence · 2023Article
- BDL-SP: A Bio-inspired DL model for the identification of altered Signaling Pathways in Multiple Myeloma using WES data.American journal of cancer research · 2023Article
- DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.Communications biology · 2022Article
- Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts.Nucleic acids research · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When trying to identify new potential therapeutic protein targets, access to data and knowledge is increasingly important. In a field where new resources and data sources become available every day, it is crucial to be able to take a step back and look at the wider picture in order to identify potential drug targets. While this task is routinely performed by bespoke literature searches, it is often time-consuming and lacks uniformity when comparing multiple targets at one time. To address this challenge, we developed TargetDB, a tool that aggregates public information available on given target(s) (links to disease, safety, 3D structures, ligandability, novelty, etc.) and assembles it in an easy to read output ready for the researcher to analyze. In addition, we developed a target scoring system based on the desirable attributes of good therapeutic targets and machine learning classification system to categorize novel targets as having promising or challenging tractrability. In this manuscript, we present the methodology used to develop TargetDB as well as test cases.
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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.