ArticleACS nano2025
TuNa-AI: A Hybrid Kernel Machine To Design Tunable Nanoparticles for Drug Delivery.
Article in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Supramolecular Degraders: An Emerging Paradigm in Targeted Protein Degradation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Nanoparticles for Drug Delivery: Design, Mechanisms, and Clinical Translation.Molecules (Basel, Switzerland) · 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
- Engineered Neutrophils in Translational Medicine: Gene Editing, Nanotechnology, and AI-Driven Clinical Breakthroughs.Advanced healthcare materials · 2026Review
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Iron-Based Nanoparticles as Delivery Tools.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Review
- Could artificial intelligence gradually replace classical adjuvants?Frontiers in immunology · 2026Review
- Advances in Nanotechnology-Based Immunomodulatory Strategies for the Treatment of Allergic Rhinitis.International journal of nanomedicine · 2026Review
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- Expanding the CRISPR/Cas toolkit: applications in proteomics and theranostics.Frontiers in bioengineering and biotechnology · 2025Review
- Artificial intelligence-guided nanoparticle design for advanced targeted drug delivery.BioImpacts : BI · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Artificial intelligence (AI) has the potential to transform nanoparticle development for drug delivery; however, existing strategies typically optimize either material selection or component ratios in isolation. To enable simultaneous optimization of both, we integrated an automated liquid handling platform with machine learning to systematically explore the nanoparticle formulation space. A data set comprising 1275 distinct formulations (spanning drug molecules, excipients, and synthesis molar ratios) was generated, resulting in a 42.9% increase in successful nanoparticle formation through composition optimization. We developed a bespoke hybrid kernel machine that couples molecular feature learning with relative compositional inference, enhancing the modeling of formulation outcomes across chemical spaces. This hybrid kernel significantly improved prediction performance across three kernel-based algorithms, with a support vector machine (SVM) achieving superior performance when using our kernel compared to standard kernels and outperforming all other machine learning architectures, including transformer-based deep neural networks. Using SVM-guided predictions, we successfully formulated the difficult-to-encapsulate venetoclax with optimized taurocholic acid ratios, yielding enhanced
Indexed as
Identifiers
What Socratic 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 Socratic graph.