ReviewActa pharmaceutica Sinica. B2026
Machine learning reshapes the paradigm of nanomedicine research.
Review in Acta pharmaceutica Sinica. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Nanomaterials targeting ferroptosis for osteoarthritis treatment: a systematic review of preclinical evidence.Journal of nanobiotechnology · 2026Pooled it
- Engineering Functional Biomaterials for Targeted and Localized Cancer Drug Delivery: A Structure-Property-Performance Design Perspective.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Multifunctional Nano-Contrast Agent Carriers: From Traditional Platforms to Next-Generation Theranostic Applications in Molecular Imaging.Biomedicines · 2026Review
- Editorial of special column on machine learning in drug discovery.Acta pharmaceutica Sinica. B · 2026Article
- Engineering smart polymeric lipid nanoparticles for breast cancer: AI-guided formulation design, biological barriers, and translational constraints.Journal of nanobiotechnology · 2026Review
- Few-shot learning for classification of SEM images from green-synthesized nanoparticles of Momordica cymbalaria.Scientific reports · 2026Article
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Nanoparticles in HIV treatment for improved drug delivery, clinical translation, and future direction.Discover nano · 2026Review
- Tumor Microenvironment-Responsive Nanomedicine: Monitoring and Modulating the Tumor Microenvironment for Precision Cancer Therapy.International journal of nanomedicine · 2026Review
- Structural-Functional Customization of Nanoscale Liposome-in-Liposome Systems: Precision Engineering Methodology and Artificial-Intelligence-Driven Design Prospects.Research (Washington, D.C.) · 2026Article
- AI-Integrated Micro/Nanorobots for Biomedical Applications: Recent Advances in Design, Fabrication, and Functions.Biosensors · 2025Review
- Nanomaterials in Drug Delivery: Leveraging Artificial Intelligence and Big Data for Predictive Design.International journal of molecular sciences · 2025Review
- Nanotechnology Driven Innovations in Modern Pharmaceutics: Therapeutics, Imaging, and Regeneration.Nanomaterials (Basel, Switzerland) · 2025Review
- Recent Trends in Bioinspired Metal Nanoparticles for Targeting Drug-Resistant Biofilms.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Nanomaterial-Enhanced Immunotherapy: Advancing T-Cell-Based Treatments for Bladder Cancer.International journal of nanomedicine · 2025Review
- Clinical trials of nanoparticle-enhanced CAR-T and NK cell therapies in oncology: overcoming translational and clinical challenges - a mini review.Frontiers in medicine · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanodrug delivery systems (NDDS) have demonstrated outstanding performance in drug delivery due to their efficient delivery capacity, targeting ability, and biocompatibility. However, the development of nanomedicines still heavily relies on the expertise of formulation scientists and extensive trial-and-error experiments. Despite the abundance of data in nanoscience, traditional biological research often struggles to effectively process, analyze, and utilize these datasets, limiting nanomedicine studies to a "one-to-one" approach. Against this backdrop, the rapid growth of artificial intelligence (AI) and machine learning (ML) offers a new paradigm for nanomedicine research. Unlike traditional statistical analyses and mathematical models, AI and ML provide deeper insights into big data, enhancing the efficiency of nanomedicine development while steering the field toward more intelligent and more precise research approaches. This review focuses on milestone studies that use ML to reshape nanomedicine research from a pharmaceutics perspective, highlighting how data-driven ML models can guide new directions in nanomedicine development.
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.