ArticleNature biomedical engineering2019
Exploration of the nanomedicine-design space with high-throughput screening and machine learning.
Article in Nature biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 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
100 citing papers in PubMed.
- Impact of Positively Charged Backbone Modifications on the Properties of Spherical Nucleic Acids.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Nanomedicine beyond carriers - devices, cells & living therapeutics.Biomedical microdevices · 2026Review
- Living Inorganic Nanomaterials: Design, Preparation, and Biomedical Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- The landscape of genitourinary cancer vaccines: clinical advances and future opportunities.Journal of advanced research · 2026Review
- Design of Spherical Nucleic Acids: From Fast Synthesis to Structural Engineering.Biomolecules · 2026Review
- Nanoengineering Systems for Gene Therapy: Mechanisms, Modalities, and Future Directions.International journal of molecular sciences · 2026Review
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Biogenic Metal Nanoparticles from Indian Flora as Programmable Bio-Interfaces: From Phytochemical Coronas to Precision Nanomedicine.International journal of molecular sciences · 2026Review
- Design of Single-Atom Nanozymes for Precision Treatment of Erectile Dysfunction with Integrated Single-Cell RNA Sequencing and Machine Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multiobjective AI model for LNP engineering enhances tissue-selective mRNA delivery.Nature biotechnology · 2026Article
- Spherical Nucleic Acids: Turning Synthetic Advances and Fundamental Discovery into Translational Breakthroughs in Chemistry, Materials Development, Biology, and Medicine.Accounts of chemical research · 2026Article
- E7Science advances · 2026Article
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- Atom-level descriptors and explainable prediction of iron carbide nanoparticles' cytotoxicityNanoscale advances · 2026Article
- Modelling and estimation of chemical reaction yields from high-throughput experiments.Communications chemistry · 2026Article
- Artificial intelligence-enabled cross-scale integration of traditional Chinese medicine and biomedicine for sepsis: from mechanisms to delivery.Frontiers in pharmacology · 2026Review
- Engineering spherical nucleic acids for precision cancer therapy: design strategies and medical applications.Theranostics · 2026Review
- AI and organoid platforms for brain-targeted theranostics.Theranostics · 2026Review
- Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.Drug delivery · 2025Article
- Structural immunotherapy: Harnessing chemical design to build powerful next-generation therapeutics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
40 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Only a tiny fraction of the nanomedicine-design space has been explored, owing to the structural complexity of nanomedicines and the lack of relevant high-throughput synthesis and analysis methods. Here, we report a methodology for determining structure-activity relationships and design rules for spherical nucleic acids (SNAs) functioning as cancer-vaccine candidates. First, we identified ~1,000 candidate SNAs on the basis of reasonable ranges for 11 design parameters that can be systematically and independently varied to optimize SNA performance. Second, we developed a high-throughput method for making SNAs at the picomolar scale in a 384-well format, and used a mass spectrometry assay to rapidly measure SNA immune activation. Third, we used machine learning to quantitatively model SNA immune activation and identify the minimum number of SNAs needed to capture optimum structure-activity relationships for a given SNA library. Our methodology is general, can reduce the number of nanoparticles that need to be tested by an order of magnitude, and could serve as a screening tool for the development of nanoparticle therapeutics.
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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.