ReviewDrug discovery today2025
AI-powered programmable virtual humans toward human physiologically-based drug discovery.
Review in Drug discovery today, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- GEM-GPT Enables Personalized Cell Type-Resolved Therapeutic Design for Systems Pharmacology.bioRxiv : the preprint server for biology · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 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
3 authors.
Funding
Abstract
Artificial intelligence (AI) has generated great interest in drug discovery, but current approaches merely digitize existing experiments, failing to predict clinical outcomes of new compounds. Likewise, pharmacology digital twins, designed for late-phase drug development, lack the ability to bridge translational gaps, limiting their value in early-stage drug discovery. The true potential of AI lies in enabling virtual experiments impossible in the real world, 'testing' novel compounds directly within the human body. Advances in AI, high-throughput assays, and single-cell and spatial omics now enable programmable virtual humans: dynamic, multiscale models that establish a new paradigm of physiologically-based drug discovery. This approach offers a transformative path to evaluate and optimize compound efficacy and safety earlier than ever in the drug discovery process.
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.