Prediction of cellulose dissolution in ionic liquids using molecular descriptors based QSAR model

A machine learning-based QSAR model reveals important molecular features for understanding the potential inhibition mechanism of ionic liquids to acetylcholinesterase

The broad application of ionic liquids (ILs) has been hindered by uncertainties surrounding their ecotoxicity. In this work, a Quantitative Structure-Activity Relationship (QSAR) model was devised to predict the inhibition of ILs towards the activity of AChE, employing both Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) machine learning approaches. Fourteen kings of essential molecular feature descriptors were screened from an initial roster of 244 descriptors through the application of a feature importance index and they showed a significant impact on the activity of AChE activity. The two models based solely on the 14 most critical molecular descriptors could maintain model's robustness and reliability. The correlation analysis between these 14 descriptors and the inhibition of AChE activity revealed the potential impact of the molecular characteristics on ILs toxicity. The results underscored the main influence of cations in ILs on the inhibitory activity towards the AChE enzyme. Specifically, cations exhibiting hydrophobicity properties were found to exert more potent inhibitory effects on the AChE enzyme. In addition, some other properties of the cations, such as the degree of branching, atomic weight and partial charge also modulated their inhibition potential. This study enhances the comprehension of the structure-activity relationship between ILs and AChE inhibition, providing a reference for designing safer and greener ILs.

Ionic Liquid-Based Surfactants: Recent Advances in Their Syntheses, Solution Properties, and Applications

The impetus for the expanding interest in ionic liquids (ILs) is their favorable properties and important applications. Ionic liquid-based surfactants (ILBSs) carry long-chain hydrophobic tails. Two or more molecules of ILBSs can be joined by covalent bonds leading, e.g., to gemini compounds (GILBSs). This review article focuses on aspects of the chemistry and applications of ILBSs and GILBSs, especially in the last ten years. Data on their adsorption at the interface and micelle formation are relevant for the applications of these surfactants. Therefore, we collected data for 152 ILBSs and 11 biamphiphilic compounds. The head ions of ILBSs are usually heterocyclic (imidazolium, pyridinium, pyrrolidinium, etc.). Most of these head-ions are also present in the reported 53 GILBSs. Where possible, we correlate the adsorption/micellar properties of the surfactants with their molecular structures, in particular, the number of carbon atoms present in the hydrocarbon "tail". The use of ILBSs as templates for the fabrication of mesoporous nanoparticles enables better control of particle porosity and size, hence increasing their usefulness. ILs and ILBSs form thermodynamically stable water/oil and oil/water microemulsions. These were employed as templates for (radical) polymerization reactions, where the monomer is the "oil" component. The formed polymer nanoparticles can be further stabilized against aggregation by using a functionalized ILBS that is co-polymerized with the monomers. In addition to updating the literature on the subject, we hope that this review highlights the versatility and hence the potential applications of these classes of surfactants in several fields, including synthesis, catalysis, polymers, decontamination, and drug delivery.

Solubility predictions through LSBoost for supercritical carbon dioxide in ionic liquids

The LSBoost model is developed to predict the solubility of supercritical carbon dioxide in 24 ionic liquids by using critical properties and biphasic system parameters as descriptors. The model is highly accurate and stable.

QSAR modelling using combined simple competitive learning networks and RBF neural networks

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.

Biological Activity of Ionic Liquids and Their Application in Pharmaceutics and Medicine

Ionic liquids are remarkable chemical compounds, which find applications in many areas of modern science. Because of their highly tunable nature and exceptional properties, ionic liquids have become essential players in the fields of synthesis and catalysis, extraction, electrochemistry, analytics, biotechnology, etc. Apart from physical and chemical features of ionic liquids, their high biological activity has been attracting significant attention from biochemists, ecologists, and medical scientists. This Review is dedicated to biological activities of ionic liquids, with a special emphasis on their potential employment in pharmaceutics and medicine. The accumulated data on the biological activity of ionic liquids, including their antimicrobial and cytotoxic properties, are discussed in view of possible applications in drug synthesis and drug delivery systems. Dedicated attention is given to a novel active pharmaceutical ingredient-ionic liquid (API-IL) concept, which suggests using traditional drugs in the form of ionic liquid species. The main aim of this Review is to attract a broad audience of chemical, biological, and medical scientists to study advantages of ionic liquid pharmaceutics. Overall, the discussed data highlight the importance of the research direction defined as "Ioliomics", studies of ions in liquids in modern chemistry, biology, and medicine.

Enhanced Swelling and Responsive Properties of Pineapple Peel Carboxymethyl Cellulose-g-poly(acrylic acid-co-acrylamide) Superabsorbent Hydrogel by the Introduction of Carclazyte

The superabsorbent hydrogels were synthesized by grafting acrylic acid and acrylamide onto pineapple peel carboxymethyl cellulose and effect of carclazyte introduction was compared. The structure and morphology of the superabsorbents were investigated by Fourier transform infrared spectroscopy, X-ray diffraction, and field emission scanning electron microscopy. Swelling behaviors of the superabsorbents were investigated in distilled water, 0.9% NaCl solution, various salt and pH solutions, as well as surfactant solutions and simulated physiological fluids. The swelling dynamic mechanism of the superabsorbents was explained well by Fickian diffusion and Schott's pseudo-second-order models. The introduction of carclazyte effectively improved the swelling capacity of the superabsorbents in various solutions as well as its salt- and pH-sensitivity. The prepared superabsorbents also exhibited excellent sensitivities to various surfactant solutions and simulated physiological fluids, showing potential applications in the biomaterials field.