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Solubility

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A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.

BMC medical informatics and decision making
BACKGROUND: The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-friendly characteristics. The c...

Enhancing Protein Solubility via Glycosylation: From Chemical Synthesis to Machine Learning Predictions.

Biomacromolecules
Glycosylation is a valuable tool for modulating protein solubility; however, the lack of reliable research strategies has impeded efficient progress in understanding and applying this modification. This study aimed to bridge this gap by investigating...

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles.

Molecular pharmaceutics
Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polyme...

Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen.

Molecules (Basel, Switzerland)
Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or bet...

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions.

Molecular pharmaceutics
This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated no...

Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation.

International journal of pharmaceutics
A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent typ...

Computational design of soluble and functional membrane protein analogues.

Nature
De novo design of complex protein folds using solely computational means remains a substantial challenge. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topolog...

Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface.

AAPS PharmSciTech
Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low solubility and low bioavailability limit its effectiveness in clinical practice. Here, we developed a c...

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology.

Journal of chemical information and modeling
Rapid and accurate calculation of acid dissociation constant (p) is crucial for designing chemical synthesis routes, optimizing catalysts, and predicting chemical behavior. Despite recent progress in machine learning, predicting solvation acidity, es...

Enhancing Predictions of Drug Solubility Through Multidimensional Structural Characterization Exploitation.

IEEE journal of biomedical and health informatics
Solubility is not only a significant physical property of molecules but also a vital factor in small-molecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resou...