ReviewNature communications2023
Applied machine learning as a driver for polymeric biomaterials design.
Review in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 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
58 citing papers in PubMed.
- Functionalized biomaterial platforms for interventional embolization therapy of hepatocellular carcinoma.Bioactive materials · 2027Review
- Machine learning-guided discovery of a bioactive hydrogel for spatiotemporally orchestrated tendon-to-bone healing.Bioactive materials · 2026Article
- Polymeric Materials in Cancer Immunotherapy: Advances, Challenges, and Future Directions.Polymer science & technology (Washington, D.C.) · 2026Review
- Article
- Cancer Drug Delivery with Nanoparticles and Biomolecules: Stimuli-Responsive, Theranostic, and AI-Guided Approaches.Materials (Basel, Switzerland) · 2026Review
- Soft, Reactive, and Alive: A Dynamic Framework for Degradation and Functional Stability of Polymeric Biomaterials.Polymers · 2026Review
- Machine learning-driven optimization of high-velocity impact resistance for three-dimensional biphasic composites.Nature communications · 2026Article
- MC-NODE: A Mechanism-Decomposed Neural Differential Model for PLGA Microsphere Drug Release Prediction and Attribution.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Living Inorganic Nanomaterials: Design, Preparation, and Biomedical Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- Self-driving lab for the data-driven design of single-chain polymer nanoparticles.Cell press blue · 2026Article
- Microengineered Gradient Hydrogels for Mechanobiology.Advanced healthcare materials · 2026Review
- Biophysical signal-driven scaffold design for stem cell-guided osteochondral regeneration.Bioactive materials · 2026Review
- Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.Gels (Basel, Switzerland) · 2026Review
- Artificial intelligence for drug delivery: Yesterday, today and tomorrow.Acta pharmaceutica Sinica. B · 2026Review
- Understanding Fabrication Variability in Core-Shell Soft Biomaterials Using Stochastic Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.Journal of the American Chemical Society · 2026Article
- Review
- Three-tier framework for high-throughput biofabrication: Integrating 3D bioprinting, assistive platforms, and translational opportunities.Bioactive materials · 2026Review
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
Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions.
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.