AIMC Topic: Solubility

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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...

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...

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...

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...

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...

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...

Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective.

Journal of chemical information and modeling
Absorption, distribution, metabolism, and excretion (ADME), which collectively define the concentration profile of a drug at the site of action, are of critical importance to the success of a drug candidate. Recent advances in machine learning algori...

Henry gas solubility optimization double machine learning classifier for neurosurgical patients.

PloS one
This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several...

Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Journal of chemical information and modeling
Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate fr...

Convolutional neural network-based evaluation of chemical maps obtained by fast Raman imaging for prediction of tablet dissolution profiles.

International journal of pharmaceutics
In this work, the capabilities of a state-of-the-art fast Raman imaging apparatus are exploited to gain information about the concentration and particle size of hydroxypropyl methylcellulose (HPMC) in sustained release tablets. The extracted informat...