ReviewF1000Research2020
Recent advances in phenotypic drug discovery.
Review in F1000Research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Article
- A novel small molecule remyelination therapy for multiple sclerosis.npj drug discovery · 2026Article
- Review
- Review
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Hyperkalemia in Severe Burn Injuries: Pathogenesis and Therapeutic Advances.Journal of community hospital internal medicine perspectives · 2026Review
- Ligand-Based Compound Activity Prediction via Few-Shot Learning.Journal of chemical information and modeling · 2024Article
- Stability-based approaches in chemoproteomics.Expert reviews in molecular medicine · 2024Review
- AI enhances drug discovery and development.National science review · 2024Article
- Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening.Heliyon · 2024Review
- Drug repurposing and phenotypic screening: innovative strategies for treating ultra-rare disorders.Frontiers in medicine · 2024Article
- Discovery of a Small-Molecule Modulator of the Autophagy-Lysosome Pathway That Targets Lamin A/C and LAMP1, Induces Autophagic Flux, and Affects Lysosome Positioning in Neurons.ACS chemical neuroscience · 2023Article
- μMap Photoproximity Labeling Enables Small Molecule Binding Site Mapping.Journal of the American Chemical Society · 2023Article
- Sequence enrichment profiles enable target-agnostic antibody generation for a broad range of antigens.Cell reports methods · 2023Article
- Computer vision meets microfluidics: a label-free method for high-throughput cell analysis.Microsystems & nanoengineering · 2023Review
- Phenotypic drug discovery: recent successes, lessons learned and new directions.Nature reviews. Drug discovery · 2022Review
- Hypothesis-generating proteome perturbation to identify NEU-4438 and acoziborole modes of action in the African Trypanosome.iScience · 2022Article
- Repurposing drugs to treat cardiovascular disease in the era of precision medicine.Nature reviews. Cardiology · 2022Review
- Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer's disease drug repurposing.PLoS computational biology · 2022Article
- Reinforcement learning for systems pharmacology-oriented and personalized drug design.Expert opinion on drug discovery · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is a great need for innovative new medicines to treat unmet medical needs. The discovery and development of innovative new medicines is extremely difficult, costly, and inefficient. In the last decade, phenotypic drug discovery (PDD) was reintroduced as a strategy to provide first-in-class medicines. PDD uses empirical, target-agnostic lead generation to identify pharmacologically active molecules and novel therapeutics which work through unprecedented drug mechanisms. The economic and scientific value of PDD is exemplified through game-changing medicines for hepatitis C virus, spinal muscular atrophy, and cystic fibrosis. In this short review, recent advances are noted for the implementation and de-risking of PDD (for compound library selection, biomarker development, mechanism identification, and safety studies) and the potential for artificial intelligence. A significant barrier in the decision to implement PDD is balancing the potential impact of a novel mechanism of drug action with an under-defined scientific path forward, with the desire to provide infrastructure and metrics to optimize return on investment, which a known mechanism provides. A means to address this knowledge gap in the future is to empower precompetitive research utilizing the empirical concepts of PDD to identify new mechanisms and pharmacologically active compounds.
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.