AIMC Topic: Solvents

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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...

Application of Neural Network in Predicting HS from an Acid Gas Removal Unit (AGRU) with Different Compositions of Solvents.

Sensors (Basel, Switzerland)
The gas sweetening process removes hydrogen sulfide (HS) in an acid gas removal unit (AGRU) to meet the gas sales' specification, known as sweet gas. Monitoring the concentration of HS in sweet gas is crucial to avoid operational and environmental is...

Dip-coating electromechanically active polymer actuators with SIBS from midblock-selective solvents to achieve full encapsulation for biomedical applications.

Scientific reports
Soft and compliant ionic electromechanically active polymer actuators (IEAPs) are a promising class of smart materials for biomedical and soft robotics applications. These materials change their shape in response to external stimuli like the electric...

Prediction of biphasic separation in CO absorption using a molecular surface information-based machine learning model.

Environmental science. Processes & impacts
Carbon dioxide capture technologies have become a focus to overcome global warming. Biphasic absorbents are one of the promising approaches for energy-saving CO capture processes. These biphasic absorbents are mainly composed of a mixed solvent compo...

Combination of explainable machine learning and conceptual density functional theory: applications for the study of key solvation mechanisms.

Physical chemistry chemical physics : PCCP
We present explainable machine learning approaches for the accurate prediction and understanding of solvation free energies, enthalpies, and entropies for different salts in various protic and aprotic solvents. As key input features, we use fundament...

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...

Machine learning prediction of empirical polarity using SMILES encoding of organic solvents.

Molecular diversity
Machine learning based statistical models have played a significant role in increasing the speed and accuracy with which the chemical and physical properties of chemical compounds can be predicted as compared to the experimental, and traditional ab i...

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 ...

Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions.

Physical chemistry chemical physics : PCCP
Efficient prediction of the partition coefficient (log ) between polar and non-polar phases could shorten the cycle of drug and materials design. In this work, a descriptor, named 〈 - ACSFs〉, is proposed to take the explicit polarization effects in t...

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Proceedings of the National Academy of Sciences of the United States of America
Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemi...