AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Solubility

Showing 41 to 50 of 146 articles

Clear Filters

Adapting physiologically-based pharmacokinetic models for machine learning applications.

Scientific reports
Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine le...

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS).

European journal of drug metabolism and pharmacokinetics
BACKGROUND AND OBJECTIVE: The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transpor...

Ensemble Geometric Deep Learning of Aqueous Solubility.

Journal of chemical information and modeling
Geometric deep learning is one of the main workhorses for harnessing the power of big data to predict molecular properties such as aqueous solubility, which is key to the pharmacokinetic improvement of drug candidates. Two ensembles of graph neural n...

Rapid screening of ternary amorphous formulations by a spray drying robot.

International journal of pharmaceutics
Spray drying is commonly used for producing amorphous solid dispersions to improve drug solubility. The development of such formulations typically relies on comprehensive excipient and composition screening, which requires the preparation of many spr...

Solubility of dapsone in deep eutectic solvents: Experimental analysis, molecular insights and machine learning predictions.

Polimery w medycynie
BACKGROUND: Dapsone (DAP) is an anti-inflammatory and antimicrobial active pharmaceutical ingredient used to treat, e.g., AIDS-related diseases. However, low solubility is a feature hampering its efficient use.

Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation.

Computers in biology and medicine
Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a c...

The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge.

SLAS discovery : advancing life sciences R & D
The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to predict the aqueous solubility of small molecules using experimental data from 100 K compounds. In total, hundred teams took part in the challenge to predict low, m...

Outline and background for the EU-OS solubility prediction challenge.

SLAS discovery : advancing life sciences R & D
In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it...

Machine vision-based non-destructive dissolution prediction of meloxicam-containing tablets.

International journal of pharmaceutics
Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by...

Calculation of solvation force in molecular dynamics simulation by deep-learning method.

Biophysical journal
Electrostatic calculations are generally used in studying the thermodynamics and kinetics of biomolecules in solvent. Generally, this is performed by solving the Poisson-Boltzmann equation on a large grid system, a process known to be time consuming....