ReviewNature medicine2025
Artificial intelligence in drug development.
Review in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 227 papers, 1 of them a synthesis that pooled it.
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
227 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI in pharmacy education: a comparative visualization analysis of global and Chinese research trends.Frontiers in medicine · 2026Pooled it
- Artificial intelligence-guided nanozyme engineering for chronic wound healing: from rational design to precision therapeutics.Bioactive materials · 2027Review
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- In vivo immune cell engineering from bench to clinical reality.Pharmaceutical science advances · 2026Review
- Design, synthesis, and biological evaluation of 6-aryl-3-(3,4,5-trimethoxyphenyl)imidazo[1,2-a]pyridine derivatives as novel tubulin inhibitors with potent anticancer efficacy.Journal of enzyme inhibition and medicinal chemistry · 2026Article
- Breaking through the radiation dilemma: development and clinical translation of anti-radiation drugs.Pharmaceutical science advances · 2026Review
- Perspectives for pharmaceutical screening at XFEL sources using the example of Lassa virus endonuclease.Acta crystallographica. Section D, Structural biology · 2026Article
- Accelerated discovery of thermostable mRNA-lipid nanoparticle vaccines using data-efficient AI.Nature biotechnology · 2026Article
- The next phase of translational medicine must bridge the gap between innovation to impact.Nature medicine · 2026Article
- MEGPNM as a multiscale edge-aware GAT network with hybrid pooling predicting permeability of non-peptidic macrocycles.Communications chemistry · 2026Article
- Personalized medicine, scientific revolution or self-serving illusion: viewpoint from EORTC-PAMM Group.ESMO open · 2026Review
- Polymeric Materials in Cancer Immunotherapy: Advances, Challenges, and Future Directions.Polymer science & technology (Washington, D.C.) · 2026Review
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Insights into the Complexities of Pharmacotherapy Parameters in Artificial Intelligence Models for Drug Selection, Precision Personalised Medicine and Optimal Therapeutic Outcomes.Pharmaceutics · 2026Review
- Review
- The role of classical protein tyrosine phosphatases in diabetic kidney disease and therapeutic implications.Journal of biomedical science · 2026Review
- New thinking for the next generation of antimalarials.EMBO molecular medicine · 2026Review
- Task-adaptive multimodal molecular representations for structure-sensitive property prediction.Chemical science · 2026Article
- Development and evaluation of a large language model-based, retrieval-augmented generation application for query response in early oncology clinical trials.ESMO real world data and digital oncology · 2026Article
- A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
167 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.
Indexed as
Identifiers
39833407What 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.