AIMC Topic: Solubility
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Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO.
Scientific reports
Feb 12, 2025
Accurate estimation of the solubility of solid drugs (SDs) in the supercritical carbon dioxide (SC-CO) plays an essential role in the related technologies. In this study, artificial intelligence models (AIMs) by gene expression programming (GEP) and ...
Correlation of rivaroxaban solubility in mixed solvents for optimization of solubility using machine learning analysis and validation.
Scientific reports
Feb 8, 2025
In this study, the solubility of rivaroxaban, a poorly water-soluble drug, was investigated in mixed solvent systems to address challenges in pharmaceutical formulation and bioavailability enhancement. Solubility optimization is essential for the eff...
Determination of 5-fluorouracil anticancer drug solubility in supercritical COusing semi-empirical and machine learning models.
Scientific reports
Feb 7, 2025
In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO. This important rol...
Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles.
Scientific reports
Feb 4, 2025
This paper investigates delivery of encapsulated drug from poly lactic-co-glycolic micro-/nano-particles. Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component an...
Machine Learning-Based Prediction of Drug Solubility in Lipidic Environments: The Sol_ME Tool for Optimizing Lipid-Based Formulations with a Preliminary Apalutamide Case Study.
AAPS PharmSciTech
Feb 3, 2025
Lipid-based formulations are essential for enhancing drug solubility and bioavailability, yet selecting optimal lipid excipients for specific drugs remains challenging. This study introduces Sol_ME, a machine learning-based model designed to predict ...
Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.
Scientific reports
Jan 17, 2025
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...
Active learning and Gaussian processes for the development of dissolution models: An AI-based data-efficient approach.
Journal of controlled release : official journal of the Controlled Release Society
Jan 13, 2025
In vitro dissolution testing plays a key role in controlling the quality and optimizing the formulation of solid dosage pharmaceutical products. Data-driven dissolution models can improve the efficiency of testing: their predictions can act as surrog...
Preparation and optimisation of solid lipid nanoparticles of rivaroxaban using artificial neural networks and response surface method.
Journal of microencapsulation
Jan 5, 2025
AIMS: This study aimed to improve rivaroxaban delivery by optimising solid lipid nanoparticles (SLN) for minimal mean diameter and maximal entrapment efficiency (EE), enhancing solubility, bioavailability, and the ability to cross the blood-brain bar...
Hierarchical Graph Attention Network with Positive and Negative Attentions for Improved Interpretability: ISA-PN.
Journal of chemical information and modeling
Dec 9, 2024
With the advancement of deep learning (DL) methods in chemistry and materials science, the interpretability of DL models has become a critical issue in elucidating quantitative (molecular) structure-property relationships. Although attention mechanis...
FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.
Molecular pharmaceutics
Dec 8, 2024
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubi...
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