ReviewNPJ digital medicine2025
AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Review
- Artificial Intelligence as a Discovery Engine for Routine Molecular Techniques: Extracting Biological Insight from Western Blotting, ELISA, Immunostaining, and Immunoprecipitation.Cell biochemistry and biophysics · 2026Review
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- DRIVE: a comprehensive resource deciphering drug-induced transcriptomic and splicing response in cancer cell.Neoplasia (New York, N.Y.) · 2026Article
- Organoid intelligence: a promising paradigm for autism spectrum disorder research.Molecular psychiatry · 2026Review
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Cancer drug response and resistance: molecular mechanisms and combating strategies.Signal transduction and targeted therapy · 2026Review
- Envisioning population-scale immune multi-omics atlas projects.Clinical and translational medicine · 2026Article
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- From microfluidics to nanodelivery: artificial intelligence reshapes neuropharmacology research strategies.Frontiers in pharmacology · 2026Review
- Organoid models: reshaping the paradigm for precision development and evaluation of CAR-T cell therapies.Frontiers in bioengineering and biotechnology · 2026Review
- An organoid-guided roadmap for precision delivery of epigallocatechin gallate in oral submucous fibrosis.Frontiers in bioengineering and biotechnology · 2026Review
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
- Artificial intelligence and machine learning in immunosenescence: from biomarker discovery to clinical translation.Frontiers in aging · 2026Review
- Artificial intelligence as decision support for adolescent depression and anxiety: a mini review of clinical utility, safety, and implementation.Frontiers in psychiatry · 2026Review
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
AI-driven virtual cell models show the potential to transform the paradigm of life sciences research by integrating multimodal omics data (e.g., single-cell transcriptomics and proteomics) with advanced algorithms such as deep generative models and graph neural networks to enable high-precision predictions of drug responses, gene perturbations, and disease progression. These models enable high-precision predictions of drug responses, gene perturbations, and disease progression. This review outlines the technical pathways and validation mechanisms of virtual cells, emphasizing a closed-loop workflow from computational evaluation to experimental verification using CRISPR assays and organoid platforms. The applications of virtual cells in personalized drug screening and disease modeling are highlighted, showcasing their potential to reduce animal testing and optimize therapy. However, challenges in regulatory acceptance, data privacy, and model interpretability remain. Global policy and standardization trends are driving clinical translation, and future advancements will involve cross-disciplinary integration and greater standardization to enhance the impact of virtual cells in precision medicine and drug discovery.
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.