ReviewSignal transduction and targeted therapy2022
Artificial intelligence in cancer target identification and drug discovery.
Review in Signal transduction and targeted therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 191 papers, 2 of them syntheses 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
191 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.Journal of translational medicine · 2025Pooled it
- Identification of microbial markers associated with lung cancer based on multi-cohort 16 s rRNA analyses: A systematic review and meta-analysis.Cancer medicine · 2023Pooled it
- A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial.Nature medicine · 2025Trial
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- MMP2Mol: a matched molecular pairs-based framework for ligand-based de novo drug design.Briefings in bioinformatics · 2026Article
- TargetPrior: a miRNA-signature embedded evolutionary learning framework for prioritizing drug targets in acute myeloid leukemia.Bioinformatics (Oxford, England) · 2026Article
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- VEGFC as a prognostic cytokine biomarker linking lymph node metastasis to immune suppression in breast cancer.European cytokine network · 2026Article
- Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning.Interdisciplinary sciences, computational life sciences · 2026Article
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- AI-driven multi-omics drug repurposing nominates AZD7762 as a multitarget inhibitor of IL22RA1 and FAM221A in esophageal squamous cell carcinoma.NPJ precision oncology · 2026Article
- Applications and prospects of artificial intelligence and digital medicine in pediatric nasal skull base tumors.Pediatric investigation · 2026Review
- Scaffold-based evaluation metrics for fair comparison of molecular generators.Journal of cheminformatics · 2026Article
- A network medicine framework for multi-modal data integration in therapeutic target discovery.Communications chemistry · 2026Article
- Integrated single-cell and bulk transcriptomics reveals STAB1 as a novel therapeutic target for ovarian cancer.Translational oncology · 2026Article
- Locus-Specific Convergent Evolution and Interchromosomal Rearrangements Contribute to the Diversification of Amniote Type I Interferons.Evolutionary applications · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Targeting "undruggable" cancer proteins: pharmacological challenges and emerging strategies.Translational cancer research · 2026Review
131 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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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.