ReviewInternational journal of molecular sciences2025
Nanomaterials in Drug Delivery: Leveraging Artificial Intelligence and Big Data for Predictive Design.
Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Nanomedicine-based cancer immunotherapy: translational barriers, mechanistic strategies, and future perspectives.Drug delivery · 2026Review
- Toxicity of nanostructures in drug delivery applications.Pharmaceutical science advances · 2026Review
- Advances in improving cancer immunotherapy with nanotechnology: from smart nanoparticles to synergistic combination strategies.Molecular cancer · 2026Review
- AI-Assisted Engineering of Glycyrrhizic Acid/Simvastatin Nanocrystals for Multifunctional Treatment of Bacterial Osteomyelitis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Nanoparticle-Based Biomaterials in Cancer Research: From Mechanistic Insights to Therapeutic Innovation.International journal of molecular sciences · 2026Review
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Artificial intelligence-assisted design and optimization of stimuli-responsive nanocarriers for smart drug delivery.Materials today. Bio · 2026Review
- Overcoming LPS-mediated resistance in gram-negative pathogens: a review of LL-37 analogs and computational design strategies.Archives of microbiology · 2026Review
- Polymer Nanoparticles in Medical Applications-Future Directions.Nanomaterials (Basel, Switzerland) · 2026Review
- Polysaccharide-functionalized gold, silver, and iron oxide nanoparticles for siRNA delivery: The role of artificial intelligence in design and optimization.Materials today. Bio · 2026Review
- Hype vs. Health: How Approved Nanomedicines Have Met (or Missed) Early Predictions.Nanomaterials (Basel, Switzerland) · 2026Article
- Novel AI-Driven Precision Strategies in Diabetic Wound Healing: Immunomodulation and Advances in Smart Composite Nanocarriers.Pharmaceutics · 2026Review
- Nanoparticle approaches for hepatitis therapy and clinical translation.Discover nano · 2026Review
- Iridium complex-loaded biomimetic vesicles enable enhanced photodynamic therapy and immune modulation.Microsystems & nanoengineering · 2026Article
- Editorial: Pharmacological advancements of novel natural-based nanomedicines.Frontiers in pharmacology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Nanomaterials have revolutionized drug delivery by enabling precise control over solubility, stability, circulation time, and targeted release, yet translation from bench to bedside remains challenging due to complex synthesis, unpredictable biological interactions, and regulatory hurdles. Recent advances in artificial intelligence (AI) and big data analytics offer powerful solutions to these bottlenecks by integrating multidimensional datasets-encompassing physicochemical characterization, pharmacokinetics, omics profiles, and preclinical outcomes-to generate predictive models for rational nanocarrier design. Machine learning and deep learning approaches enable the prediction of key parameters such as particle size, drug loading efficiency, and biodistribution, while generative algorithms explore novel chemistries and architectures optimized for specific clinical applications. Nanoinformatics platforms and large-scale data repositories further enhance reproducibility and cross-study comparisons, supporting regulatory science and accelerating clinical translation. This review provides a comprehensive overview of nanomaterial-based drug delivery systems, highlights AI-driven strategies for predictive modeling and optimization, and discusses translational and regulatory perspectives. By bridging nanotechnology, computational modeling, and precision medicine, AI-assisted nanomaterial design has the potential to transform drug delivery into a more efficient, reproducible, and patient-centered discipline.
Indexed as
Identifiers
What 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.