ArticleScientific reports2020
Moving targets in drug discovery.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 37 citations in OpenAlex.
- The evolving landscape of drug targets.Nature reviews. Drug discovery · 2026Review
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- Temporal trends in evidence supporting novel drug target discovery.Nature communications · 2025Article
- Proteome-Wide Identification and Comparison of Drug Pockets for Discovering New Drug Indications and Side Effects.Molecules (Basel, Switzerland) · 2025Article
- The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research · 2024Article
- Combined Multiomics and In Silico Approach Uncovers PRKAR1A as a Putative Therapeutic Target in Multi-Organ Dysfunction Syndrome.ACS omega · 2023Article
- Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets.Computational and structural biotechnology journal · 2023Review
- Editorial: Advanced biophysical and biochemical technologies to study GPCR signal transduction.Frontiers in endocrinology · 2023Article
- Down the membrane hole: Ion channels in protozoan parasites.PLoS pathogens · 2022Review
- Improving Compound Activity Classification via Deep Transfer and Representation Learning.ACS omega · 2022Article
- Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes.Pharmaceuticals (Basel, Switzerland) · 2021Article
- TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disability, and schizophrenia are increasingly investigated in recent years. The methodology enables researchers to capture trends in research attention in target space at an early stage during the drug discovery process. Workflows, scripts, and data used in this study are publicly available from https://github.com/BZdrazil/Moving_Targets . An interactive web application allows the customized exploration of target, biological process, and disease trends (available at https://rguha.shinyapps.io/MovingTargets/ ).
Indexed as
Identifiers
What 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.