ReviewCambridge prisms. Precision medicine2024
AI approaches for the discovery and validation of drug targets.
Review in Cambridge prisms. Precision medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Breaking through the radiation dilemma: development and clinical translation of anti-radiation drugs.Pharmaceutical science advances · 2026Review
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- Trust, mistrust, and the promise of AI in genomics for African populations.American journal of human genetics · 2026Review
- 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
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Exploring the potential of large language models in nutrition behavior prediction: evidence from college students.Frontiers in nutrition · 2026Article
- Review
- Integrating natural product research laboratory with artificial intelligence: Advancements and breakthroughs in traditional medicine.BioMedicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.
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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.