AIMC Topic: Solubility

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Toward explicit learning frameworks for predicting the solubility of CO - N gas mixtures in brine: Implication for impure CO storage in saline aquifers.

Journal of contaminant hydrology
Carbon capture and storage (CCS) is a crucial technology for reducing industrial CO emissions and mitigating climate change. However, its large-scale deployment faces significant financial challenges, with CO capture and compression accounting for th...

MMSol: Predicting Protein Solubility with an Antinoise Multimodal Deep Model.

Journal of chemical information and modeling
Protein solubility plays a critical role in determining its biological function, such as enabling proper protein delivery and ensuring that proteins remain soluble during cellular processes or therapeutic applications. Accurate prediction of protein ...

Machine Learning Based Quantitative Structure-Dissolution Profile Relationship.

Journal of chemical information and modeling
Determining accurate drug dissolution processes in the gastrointestinal tract is critical in drug discovery as dissolution profiles provide essential information for estimating the bioavailability of orally administered drugs. While various methods h...

Analysis of drug crystallization by evaluation of pharmaceutical solubility in various solvents by optimization of artificial intelligence models.

Scientific reports
For analysis of crystallization, the solubility of drug in solvents should be correlated to input parameters. In this investigation, the solubility of salicylic acid as drug model in a variety of solvents is predicted through the utilization of multi...

Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique.

Scientific reports
This study develops and evaluates advanced hybrid machine learning models-ADA-ARD (AdaBoost on ARD Regression), ADA-BRR (AdaBoost on Bayesian Ridge Regression), and ADA-GPR (AdaBoost on Gaussian Process Regression)-optimized via the Black Widow Optim...

Computational intelligence modeling and optimization of small molecule API solubility in supercritical solvent for production of drug nanoparticles.

Scientific reports
Artificial Intelligence (AI) is applied in this research for the analysis of a novel green method for production of nanomedicine. The method is based on supercritical solvent for production of drug nanoparticles in which the AI was used to estimate t...

Application of machine learning approaches for estimating carbon dioxide absorption capacity of a variety of blended imidazolium-based ionic liquids.

Journal of molecular graphics & modelling
Ionic liquids (ILs) have gained attention in recent times as potentially effective absorbents for CO emissions owing to the number of their notable attributes, including reduced volatility, enhanced thermal consistency etc. Due to the number of chall...

Improved Solubility Predictions in scCO Using Thermodynamics-Informed Machine Learning Models.

Journal of chemical information and modeling
Accurate solubility prediction in supercritical carbon dioxide (scCO) is crucial for optimizing experimental design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive solubility database...

Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models.

Scientific reports
This study presents a comprehensive approach to predicting solubility of recombinant protein in four E. coli samples by employing machine learning techniques and optimization algorithms. Various models, including AdaBoost, Decision Tree Regression (D...

Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.

Pharmaceutical research
OBJECTIVE: Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. ...