ArticleScientific reports2024
Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Review
- Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations.Scientific reports · 2025Article
- Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models-Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)-are evaluated. Preprocessing the dataset using the Z-score approach helped to detect outliers, further improving the accuracy and dependability of the analysis. Also, Fireworks Algorithm (FWA) is employed for hyper-parameter tuning in this work. The GPR model demonstrated superior performance, achieving the lowest MSE and MAE for both drug solubility and gamma predictions, with R
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Registered trials
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