ReviewActa pharmaceutica Sinica. B2025
Graph neural networks driven acceleration in drug discovery.
Review in Acta pharmaceutica Sinica. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- From Laboratory to Patient Access: A Scoping Review of the Multi-Dimensional Challenges in Drug Repurposing.Pharmacy (Basel, Switzerland) · 2026Review
- Machine Learning-Aided Drug Repurposing for Screening COX-2 Inhibitors from Traditional Chinese Medicines.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Pharmacokinetics and Exploratory Exposure-Response Analysis of Chikusetsusaponin IVa in Myocardial Ischemia/Reperfusion-Injured Rats.Pharmaceuticals (Basel, Switzerland) · 2026Article
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Artificial Intelligence as a Disruptive Force in Pharmaceutical Innovation: Transforming Discovery, Development, and Manufacturing.Drug design, development and therapy · 2026Review
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph neural networks (GNNs) are revolutionizing drug design processes. Over the past five years, GNNs have emerged as transformative tools by accurately modeling molecular structures and interactions with binding targets. Breakthroughs in predicting molecular properties, drug repurposing, toxicity assessment, and interaction analysis, along with generative GNNs enhancing virtual screening and novel molecule design, have significantly sped up drug discovery. These GNN-driven innovations improve predictive accuracy, cut development costs, and reduce late-stage failures. This review focuses on the interdisciplinary integration of GNNs throughout the discovery process, including lead discovery and optimization, synthetic route design, drug-target interaction prediction, and molecular property profiling, while critically evaluating the challenges in translational medicine.
Indexed as
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.