ArticleScientific reports2025
Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.
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 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Development and evaluation of an effective solubility prediction model for pharmaceuticals in organic solvents using machine learning based on eXtreme Gradient Boosting.Scientific reports · 2026Article
- Computational evaluation using machine learning for analysis of membrane desalination process powered by solar energy.Scientific reports · 2025Article
- Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures.Scientific reports · 2025Article
- Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation.Scientific reports · 2025Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance. The LGB model achieved the best results, with an R
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Registered trials
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