ArticleScientific reports2025
Intelligence modeling of nanomedicine manufacture by supercritical processing in estimation of solubility of drug in supercritical CO
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
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Who cites it
9 citing papers in PubMed.
- Automated machine-learning framework for predicting drug solubility in supercritical COScientific reports · 2026Article
- Comparing machine learning and deep learning approaches to predicting the seismic response of slab-column connections.Scientific reports · 2026Article
- Data-driven prediction of lipophilic drug solubility in supercritical COFrontiers in medicine · 2026Article
- Artificial intelligence modeling of small molecule pharmaceutical solubility in supercritical fluid and comparative analysis under temperature and pressure variations.Frontiers in chemistry · 2026Article
- Application of machine learning models in pharmaceutical engineering for prediction of pharmaceuticals solubility in supercritical solvent: study on phenytoin solubility.Frontiers in chemistry · 2026Article
- From Liquid SNEDDS to Solid SNEDDS: A Comprehensive Review of Their Development and Pharmaceutical Applications.The AAPS journal · 2025Review
- Intelligence modeling of solubility of raloxifene and density of solvent for green supercritical processing of medicines for enhanced solubility.Scientific reports · 2025Article
- Raloxifene solubility in supercritical COScientific reports · 2025Article
- Predictive analysis of solubility data with pressure and temperature in assessing nanomedicine preparation via supercritical carbon dioxide.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The primary goal of this research is to apply bagging-based regression techniques to forecast the solubility of raloxifene and the density of carbon dioxide (CO₂). Bagging regression models were utilized, namely Bagging Bayesian Ridge Regression (BAG-BRR), Bagging Linear Regression (BAG-LR), and Bagging Polynomial Regression (BAG-PR). The hyperparameters of these models were tuned using the Tree-Based Parzen Estimators algorithm to achieve optimal performance. The results demonstrate the efficacy of the bagging regression models in predicting both the CO
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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.