ReviewDrug discovery today2022
Knowledge-based approaches to drug discovery for rare diseases.
Review in Drug discovery today, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Personalized drug discovery from rare diseases to cancer.npj drug discovery · 2026Review
- Loss of CD90 alters EMT-associated features and drug sensitivity in U-CH1 chordoma cells.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Dona Flor and her two husbands: Discovery of novel HDAC6/AKT2 inhibitors for myeloid cancer treatment.Computers in biology and medicine · 2025Article
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- The impact of artificial intelligence on drug discovery for neuropsychiatric disorders.EXCLI journal · 2025Review
- Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data.Scientific data · 2024Article
- Patient-Reported Experience Measurements From Individuals With Inherited Retinal Disorders Involved in Observational Research.Translational vision science & technology · 2024Article
- Would Artificial Intelligence Improve the Quality of Care of Patients With Rare Diseases?Global journal on quality and safety in healthcare · 2024Article
- The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution.Functional & integrative genomics · 2024Review
- Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases.Pharmaceutical medicine · 2024Article
- Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.Global challenges (Hoboken, NJ) · 2024Review
- Rare and complex diseases in focus: ChatGPT's role in improving diagnosis and treatment.Frontiers in artificial intelligence · 2024Article
- Artificial intelligence in rare disease diagnosis and treatment.Clinical and translational science · 2023Review
- The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders.Cureus · 2023Review
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning.Molecules (Basel, Switzerland) · 2023Article
- A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma.Frontiers in oncology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.
Indexed as
Identifiers
What Socratic 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 Socratic graph.