ArticleNature communications2024
Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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Who cites it
21 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Data-Driven Development of Biomedical Hydrogels for Controlled Drug Delivery: Clinical Applications and Emerging Machine-Learning Approaches.Journal of functional biomaterials · 2026Review
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.Gels (Basel, Switzerland) · 2026Review
- Machine learning-driven discovery of therapeutic nucleoside hydrogels for periodontitis.International journal of oral science · 2026Article
- Infection protection, immune regulation and epithelial regeneration trifunctional hydrogel for treatment of burn wounds.Materials today. Bio · 2026Article
- Nucleoside-Based Hydrogel Platform Synergizes with Photothermal Effects for Enhanced Biofilm Eradication Against Periodontitis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Interpretable Machine Learning Insights into Adhesion and Modulus of Biomedical HA-Dopamine Hydrogels.Gels (Basel, Switzerland) · 2026Article
- Quantitative Assessment of Hydrogel Printability in Extrusion Bioprinting.Gels (Basel, Switzerland) · 2026Review
- Artificial neural network as a strategy to predict rheological properties in emulgel formulations.Scientific reports · 2026Article
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- CuChemistry, an Asian journal · 2026Article
- Programmable Self-Assembly of Traditional Chinese Medicine (TCM) Monomers: From Molecular Mechanisms to Multifunctional Nanomedicine.International journal of nanomedicine · 2026Review
- Harnessing marine-derived materials for therapeutics innovations: Advances in biomaterials from the ocean.Materials today. Bio · 2025Review
- Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Enhancing fluorescent probe design through multilayer interaction convolutional networks: advancing biosensing and bioimaging precision.Chemical science · 2025Article
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Integration of deep neural network modeling and LC-MS-based pseudo-targeted metabolomics to discriminate easily confused ginseng species.Journal of pharmaceutical analysis · 2025Article
- Clustering and classification for dry bean feature imbalanced data.Scientific reports · 2024Article
- Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors.Nature communications · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69-0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag
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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.