ReviewNature reviews. Drug discovery2026
Target identification and assessment in the era of AI.
Review in Nature reviews. Drug discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- The evolving landscape of drug targets.Nature reviews. Drug discovery · 2026Review
- AI-enabled engineering of hesperidin/ursodeoxycholic acid nanomedicine for synergistic treatment of drug-induced liver injury.Smart molecules : open access · 2026Article
- Hybrid Computational Modeling with Multi-Level Validation Identifies TK1-VIM as a Robust Therapeutic Pair in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- Review
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery and development is time-intensive, expensive and laden with risk. Identifying the right drug targets is crucial for increasing the probability of success, but traditional target identification and validation often take years, and a target is only fully validated once a drug based on it receives approval by regulatory agencies. Given its proficiency in analysing large datasets and intricate biological networks, artificial intelligence (AI) is playing an increasingly important role in drug target identification and assessment. This article reviews recent advances in target discovery, emphasizing key considerations in target selection and breakthroughs in the application of AI-driven approaches for therapeutic target exploration, as well as challenges and limitations. We also highlight examples where AI tools have enabled or supported the identification of targets for which drug candidates have entered clinical trials.
Indexed as
Identifiers
42009769What Socratic 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 Socratic graph.