Analysis of small-molecule drug solubility in binary solvents at different temperatures was carried out via several machine learning models and integration of models to optimize. We investigated the solubility of rivaroxaban in both dichloromethane a...
Journal of chemical information and modeling
Aug 4, 2025
Carbon capture through physical solvents reduces energy consumption and lowers environmental impact compared with conventional chemical absorption methods. Typical properties for solvent screening are solubility and selectivity. However, they require...
Evaluating the solubility of various drugs in supercritical CO is a fundamental step in developing a supercritical process for formulating new pharmaceuticals. Atorvastatin, Lovastatin, and Simvastatin are statin drugs with limited solubility and low...
Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learn...
International journal of pharmaceutics
Jul 24, 2025
This critical review provided a detailed strengths, weaknesses, opportunities, and threats (SWOT) analysis of nebulized liposomal formulations, focusing on the strengths and translational potential. The formulations have notable strengths, such as en...
Solubility is critical in drug discovery and development, as it significantly influences a medication's bioavailability and therapeutic efficacy. Understanding solubility at the early stages of drug discovery is essential for minimizing resource cons...
Journal of chemical information and modeling
Jul 17, 2025
Graph Neural Networks (GNNs) are powerful tools for predicting chemical properties, but their black-box nature can limit trust and utility. Explainability through feature attribution and awareness of prediction uncertainty are critical for practical ...
The development of continuous pharmaceutical manufacturing is crucial and can be analyzed via advanced computational models. Machine learning is a strong computational paradigm that can be integrated into a continuous process to enhance the drugs' so...
Journal of chemical information and modeling
Jul 10, 2025
Solubility prediction is crucial for drug development and materials science, yet existing models struggle with generalizability across diverse solvents and temperatures. This study develops a novel solubility prediction model, DMPNN-MoE, which integr...
This study introduces a sophisticated predictive framework for determining drug solubility and activity values in formulations via machine learning. The framework utilizes a comprehensive dataset consisting of more than 12,000 data rows and 24 input ...
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