ArticleMethods in molecular biology (Clifton, N.J.)2024
Knowledge Graphs and Their Applications in Drug Discovery.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Medicines, Diseases, Indications, and Contraindications (MeDIC): a foundational resource to support drug repurposing.Nucleic acids research · 2026Article
- Application of knowledge graphs in rare disease research.Frontiers in public health · 2026Review
- Systematic discovery of disease-modifying targets by prediction from knowledge graph-based AI model and experimental validation: Parkinson's disease case.Computational and structural biotechnology journal · 2026Article
- Integrating text mining and knowledge graph to enhance biopharmaceutical process optimization.PloS one · 2026Article
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model.ArXiv · 2024Article
- Knowledge Graph for Breast Cancer Prevention and Treatment: Literature-Based Data Analysis Study.JMIR medical informatics · 2024Article
- Generative AI-based knowledge graphs for the illustration and development of mHealth self-management content.Frontiers in digital health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate explainable predictions, which is a key topic in contemporary artificial intelligence. In this chapter, we outline some of the factors that need to be considered when constructing biomedical knowledge graphs, examine recent advances in mining such systems to gain insights for drug discovery, and identify potential future areas for further development.
Indexed as
Identifiers
37702941What OpenQuestion holds
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.