ArticleScientific reports2025
Machine learning estimation and optimization for evaluation of pharmaceutical solubility in supercritical carbon dioxide for improvement of drug efficacy.
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 2 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
2 citing papers in PubMed.
- Machine learning-based prediction of paracetamol solubility and CO₂ density in supercritical systems using artificial rabbits optimization.Scientific reports · 2026Article
- Machine learning estimating paracetamol solubility in supercritical COScientific reports · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
This study focuses on predicting the solubility of paracetamol and density of solvent using temperature (T) and pressure (P) as inputs. The process for production of the drug is supercritical technique in which the focus was on theoretical investigations of drug solubility and solvent density as well. Machine learning models with a two-input, two-output structure were developed and validated using experimental data on paracetamol solubility as well as density. Ensemble models with decision trees as base models, including Extra Trees (ETR), Random Forest (RFR), Gradient Boosting (GBR), and Quantile Gradient Boosting (QGB) were adjusted to predict the two outputs. The results are useful to evaluate the feasibility of process in improving the efficacy of the drug, i.e., its enhanced bioavailability. The hyper-parameters of ensemble models as well as parameters of decision tree tuned using WOA algorithm separately for both outputs. The Quantile Gradient Boosting model showed the best performance for mole fraction (drug solubility), while the R
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Registered trials
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