AIMC Topic: Solvents

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Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

Scientific reports
Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independentl...

Size Control in the Nanoprecipitation Process of Stable Iodine (¹²⁷I) Using Microchannel Reactor-Optimization by Artificial Neural Networks.

AAPS PharmSciTech
In this study, nanosuspension of stable iodine ((127)I) was prepared by nanoprecipitation process in microfluidic devices. Then, size of particles was optimized using artificial neural networks (ANNs) modeling. The size of prepared particles was eval...

Design Space Exploration and Machine Learning Prediction of Hydrofluorocarbon Solubility in Ionic Liquids for Refrigerant Separation.

Journal of chemical information and modeling
Ionic liquids (ILs) are promising solvents for the separation of hydrofluorocarbon (HFC) mixtures due to their tunable solvation properties and negligible vapor pressure. We present a computational study of -32 and -125 solubility in over 341,000 ILs...

Intelligent transformation of ultrasound-assisted novel solvent extraction plant active ingredients: Tools for machine learning and deep learning.

Food chemistry
Ultrasound-assisted novel solvent extraction enhances plant bioactive compound yield via cavitation, mechanical, and thermal mechanisms. However, the high designability of novel solvents, the multiple influence factors for extracting results, the com...

AI-powered insights into the UniSpray ionization in supercritical fluid chromatography-mass spectrometry.

Journal of chromatography. A
Selection of the optimal makeup solvent composition is critical for achieving sensitive and reproducible ionization in supercritical fluid chromatography-mass spectrometry (SFC-MS). This study investigated the ionization processes in a spray-based io...

Extracting Residue Solvent Exposure from Covalent Labeling Data with Machine Learning: A Hybrid Approach for Protein Structure Prediction.

Journal of the American Society for Mass Spectrometry
Hydroxyl radical protein footprinting (HRPF) coupled with mass spectrometry yields information about residue solvent exposure and protein topology. However, data from these experiments are sparse and require computational interpretation to generate u...

Deciphering the structural complexity of esterases in Amycolatopsis eburnea: A comprehensive exploration of solvent accessibility patterns.

Computers in biology and medicine
Carboxylesterases (CES) are pivotal enzymes in the hydrolysis of carboxylic esters, playing fundamental roles in both biological systems and biotechnological applications. This study investigates CES from the Amycolatopsis genus, characterized by its...

Predicting the solubility of drugs in supercritical carbon dioxide using machine learning and atomic contribution.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
The pharmaceutical sector is aware of supercritical CO (SC-CO) as a possible replacement for problematic organic solvents. Using a novel artificial intelligence (AI) strategy to predict drug solubility using the SC-CO system mathematically has been d...

CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction.

International journal of molecular sciences
Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challen...

Prediction of the Appropriate Temperature and Pressure for Polymer Dissolution Using Machine Learning Models.

Molecular informatics
The widespread use of polymer solutions in the chemical industry poses a significant challenge in determining optimal dissolution conditions. Traditionally, researchers have relied on experimental methods to estimate the processing parameters needed ...