ReviewAdvanced drug delivery reviews2022
Merging data curation and machine learning to improve nanomedicines.
Review in Advanced drug delivery reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Engineering Oral Nanoparticles: Navigating Biological Barriers in the Gastrointestinal Tract.Pharmaceutical research · 2026Review
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- IRACEMA: A Database Management System for Bioactive Compounds Obtained and Characterized by Brazilian Researchers.ACS omega · 2026Article
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Advancing the Landscape of RNAi Nanotherapeutics for Ischemic Heart Disease.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- A Bioinspired Approach to Next-Generation Vaccines in Solid Tumors with Engineered Cell Membranes.Research (Washington, D.C.) · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Stemness regulation in prostate cancer: prostate cancer stem cells and targeted therapy.Annals of medicine · 2025Review
- Worldwide willingness to share health data high but privacy, consent and transparency paramount, a meta-analysis.NPJ digital medicine · 2025Article
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Machine learning-assisted microfluidic approach for broad-spectrum liposome size control.Journal of pharmaceutical analysis · 2025Article
- Artificial Intelligence in Molecular Optimization: Current Paradigms and Future Frontiers.International journal of molecular sciences · 2025Review
- Leveraging Artificial Intelligence and Machine Learning for Characterizing Protein Corona, Nanobiological Interactions, and Advancing Drug Discovery.Bioengineering (Basel, Switzerland) · 2025Review
- Machine Learning in Polymer Research.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Advancing Pharmaceutical Science with Artificial Neural Networks: A Review on Optimizing Drug Delivery Systems Formulation.Current pharmaceutical design · 2025Review
- Machine learning in drug delivery.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
- Exploring and Analyzing the Systemic Delivery Barriers for Nanoparticles.Advanced functional materials · 2024Article
- The Experimentalist's Guide to Machine Learning for Small Molecule Design.ACS applied bio materials · 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
6 authors.
Funding
Abstract
Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.
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.