ArticleNature materials2018
Quantitative self-assembly prediction yields targeted nanomedicines.
Article in Nature materials, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 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
93 citing papers in PubMed.
- TIM3-targeted delivery of venetoclax overcomes drug resistance and reinvigorates NK cell activity in acute myeloid leukemia.Nature cancer · 2026Article
- A Computational Strategy for Identifying Self-Assembling Food-Derived Molecules for Antiparasitic Nanotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles.Science advances · 2026Article
- AI-nanotech synergies: recent advances in sustainable bio-manufacturing.World journal of microbiology & biotechnology · 2026Review
- Bridging hydrogen-bond-stabilized polymeric coacervate core micelles for high-efficiency drug encapsulation and delivery.Nature communications · 2026Article
- Advanced Drug Delivery Strategies for Overcoming Biological Barriers: Tumor Microenvironment and Blood-Brain Barrier.Drugs in R&D · 2026Review
- Machine Learning-Informed Nano Co-Assembly Inhibits Fibroblast Activation Protein and Improves Drug Delivery in Fibrotic Tissue.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Therapeutic role of caveolin family in stem cell fate and development for management of chronic degenerative diseases: A scientometric study to an in-depth review.Journal of advanced research · 2026Review
- Structure-Selective Polydopamine Coating on Drug Nanoparticles.ACS applied materials & interfaces · 2026Article
- Self-assembling nanocomposites for smart drug delivery: towards personalized and stimuli-responsive therapeutics.RSC advances · 2026Review
- AI and organoid platforms for brain-targeted theranostics.Theranostics · 2026Review
- Tumor microenvironment-targeted PROTAC nanoparticle self-assembly broadly predicted by structural descriptors.Science advances · 2025Article
- CAV2-expressing nerves induce metabolic switch toward mitochondrial oxidative phosphorylation to promote cancer stemness.Nature communications · 2025Article
- Nanomedicines for the treatment of genitourinary neoplasms.Materials today. Bio · 2025Review
- Chiral dependence of multifunctional supramolecular materials in crop protection.Nature communications · 2025Article
- Article
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Light-driven secondary structural remodeling in biomimetic nanosystem to enhance tumor chemo-phototherapy.Materials today. Bio · 2025Article
- Phospholipase PAFAH2 Mediates Ferroptosis Surveillance and Lipid Remodeling to Promote Resistance in KEAP1 Mutant Cancers.ACS chemical biology · 2025Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
21 authors.
Funding
Abstract
Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.
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.