ReviewACS central science2026
From Prompt to Drug: Toward Pharmaceutical Superintelligence.
Review in ACS central science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges.Chemical biology & drug design · 2026Review
- Human organoids: Fit for drug discovery?Stem cell reports · 2026Review
- Active targeting towards bone cancer and metastases by pamidronate-bridged dinuclear platinum(II) complexes.Frontiers in chemistry · 2026Article
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
No grant is acknowledged in the PubMed record.
Abstract
The convergence of generative artificial intelligence (AI) platforms and automated laboratory systems is ushering in a new era of drug discovery, in which a plain-language prompt can initiate a fully autonomous, end-to-end drug development program. This article explores the recent evolution of AI technologies and presents a "prompt-to-drug" pipeline, where AI not only generates novel hypotheses and designs optimized drug candidates but also orchestrates synthesis, validation, and clinical planning in a closed-loop system. By highlighting key breakthroughs, case studies, and the technological infrastructure required for this paradigm shift, we outline a vision for scalable, efficient, and unbiased 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.