ReviewNature reviews. Drug discovery2022
Predictive validity in drug discovery: what it is, why it matters and how to improve it.
Review in Nature reviews. Drug discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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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
43 citing papers in PubMed, 88 citations in OpenAlex.
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- Oral Cavity Antibacterial Discovery Pipeline Driven by Advanced Analytical Techniques.Molecules (Basel, Switzerland) · 2026Review
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Plant-Derived Natural Products and Selective Apoptosis: A Cancer-Cell Vulnerability-State Framework from Redox Imbalance to Membrane-Ion Dysregulation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Translational assessment instruments for preclinical to first-in-human decision-making: a scoping review.Journal of translational medicine · 2026Article
- Multimodal feature fusion for molecular property classification.Journal of cheminformatics · 2026Article
- Metabolism as the biochemical language for mechanistic biomedical AI.Nature metabolism · 2026Article
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- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- A Streamlined High Performance Liquid Chromatography with Tandem Mass Spectrometry Based Workflow for Rapid Screening of Cellular Accumulation of Small Molecules.ChemMedChem · 2026Article
- Leveraging large-scale biobanks for therapeutic target discovery.HGG advances · 2026Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
- Kappa opioid receptor control of motivated behavior revisited.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Human and financial consequences of herding in oncology drug development: clinical trials of TIGIT inhibitors.BMJ oncology · 2026Article
- Target product profile and discovery and development path for novel cryptococcal disease treatments.The Journal of infection · 2025Review
- Tumor nutrient stress gives rise to a drug tolerant cell state in pancreatic cancer.bioRxiv : the preprint server for biology · 2025Article
- Application of new approach methodologies for nonclinical safety assessment of drug candidates.Nature reviews. Drug discovery · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.
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.