AIMC Topic: Solubility

Clear Filters Showing 91 to 100 of 188 articles

Application of an AI image analysis and classification approach to characterise dissolution and precipitation events in the flow through apparatus.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
Imaging and artificial intelligence (AI) approaches have been used with increasing frequency in pharmaceutical industry in recent years. Characterisation of processes such as drug dissolution and precipitation is vital in quality control testing and ...

pH-dependent solubility prediction for optimized drug absorption and compound uptake by plants.

Journal of computer-aided molecular design
Aqueous solubility is the most important physicochemical property for agrochemical and drug candidates and a prerequisite for uptake, distribution, transport, and finally the bioavailability in living species. We here present the first-ever direct ma...

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models.

Journal of chemical information and modeling
Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge" in 2019, in which competitors we...

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models.

Scientific reports
Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO) technology i...

Perspective on a chemistry classification system for AI-assisted formulation development.

Journal of controlled release : official journal of the Controlled Release Society
This perspective article draws a distinction between some of the well-known drug classification systems and a "Chemistry Classification System" (CCS). Rather than have drug classification based on some simple properties like solubility and permeabili...

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent.

Scientific reports
Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO was studied as function of pressure and temperature to assess the feasibility of that for produc...

Fast Prediction of Lipophilicity of Organofluorine Molecules: Deep Learning-Derived Polarity Characters and Experimental Tests.

Journal of chemical information and modeling
Fast and accurate estimation of lipophilicity for organofluorine molecules is in great demand for accelerating drug and materials discovery. A lipophilicity data set of organofluorine molecules (OFL data set), containing 1907 samples, is constructed ...

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds.

Molecular pharmaceutics
While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) ma...

Application of CO Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models.

Molecules (Basel, Switzerland)
Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO) for particle engineering. SCCO has great potential for application as a green and eco-friendly technique to reach small crystalline p...

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug.

Molecules (Basel, Switzerland)
The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be m...