ArticleMacromolecular rapid communications2026
Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach.
Article in Macromolecular rapid communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Predictive Modelling of Solvent Effects on Drug Incorporation into Polymeric Nanocarriers: A Machine Learning Approach.Macromolecular rapid communications · 2026Article
- Identifying Polymers that Bind or Reject Proteins with Machine Learning: Handling Categorical Features within a GPR Model.ACS polymers Au · 2026Article
- Accelerating supercritical pharmaceutical formulation via interpretable data-driven prediction of drug solubility.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This study aimed to identify solvent characteristics that enhance drug loading in polymeric micelles. Polyethylene glycol-block-polystyrene (PEG-b-PS) and curcumin were used as model compounds to investigate the impact of 40 different solvent mixtures on drug loading during flow-based assembly. We tested five algorithms: Random Forest (RF), Gradient Boosting (GP), XGBoost, Support Vector Regression (SVR), and Multilayer Perceptron (MLP), with the MLP model proving to be the most effective among them. To explain the model's predictions, we utilized SHapley Additive exPlanations (SHAP) values to identify solvent properties that contribute to high drug loading. Of the nine descriptors examined-curcumin solubility, polarity, Hildebrand solubility parameters, dipole moment, dielectric constants, viscosity, and Hansen solubility parameters (δD, δP, and δH)-solubility emerged as the most critical factor. Therefore, to achieve optimal drug loading, researchers should prioritize solvents with the highest solubility.
Indexed as
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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.