Conductor-like Screening Model For Real Solvents Research Articles

Discovery of the Most Stable Form of an Adenosine Receptor Antagonist through Virtual Polymorph Screening and Targeted Crystallization.

An innovative approach was developed to identify the optimal crystalline form, usually the thermodynamically most stable form. This method involves using virtual polymorph screening and targeted crystallization based on in silico solid-state modeling. By utilizing advanced crystal structure prediction (CSP) technology, the virtual polymorph screening method helps confirm whether the most stable crystalline form has been identified in actual crystallization experiments. If the predicted most stable form is not observed in experiments, predictions based on the method of COnductor like Screening MOdel for Real Solvents (COSMO-RS) are used to highlight solvent systems that can increase the likelihood of experimentally obtaining the desired form through a targeted crystallization process. In this work, such an approach has enabled the rapid discovery of the most stable polymorphic form and the development of a crystallization process of an adenosine receptor antagonist using minimal amounts of the sample within a shortened timeframe. Additionally, it provides a scientific rationale for ensuring the selection of the most stable form in the early stages of drug discovery, thereby reducing risks in future pharmaceutical development.

Predicting the solubility of CO2 and N2 in ionic liquids based on COSMO-RS and machine learning.

As ionic liquids (ILs) continue to be prepared, there is a growing need to develop theoretical methods for predicting the properties of ILs, such as gas solubility. In this work, different strategies were employed to obtain the solubility of CO2 and N2, where a conductor-like screening model for real solvents (COSMO-RS) was used as the basis. First, experimental data on the solubility of CO2 and N2 in ILs were collected. Then, the solubility of CO2 and N2 in ILs was predicted using COSMO-RS based on the structures of cations, anions, and gases. To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. The results show that the COSMO-RS with correction can significantly improve the prediction of CO2 solubility, and the corresponding average absolute relative deviation (AARD) is decreased from 43.4% to 11.9%. In contrast, such an option cannot improve that of the N2 dataset. Instead, the results obtained from coupling machine learning algorithms with the COSMO-RS model agree well with the experimental results, with an AARD of 0.94% for the solubility of CO2 and an average absolute deviation (AAD) of 0.15% for the solubility of N2.

Predicting hygroscopic growth of organosulfur aerosol particles using COSMOtherm

Abstract. Organosulfur (OS) compounds are important sulfur species in atmospheric aerosol particles, due to the reduction of global inorganic sulfur emissions. Understanding the physicochemical properties, such as hygroscopicity, of OS compounds is important for predicting future aerosol–cloud–climate interactions. However, their hygroscopicity is not yet well understood due to the scarcity of authentic standards. In this work, we investigated a group of OS compounds with short carbon chains (C1–C5) and oxygen-containing functional groups in the form of sodium, potassium, or ammonium salts and their mixtures with ammonium sulfate. The hygroscopic growth factors (HGFs) of these OS compounds have been experimentally studied. Here, the HGFs were calculated from mass fraction of water that was computed using the conductor-like screening model for real solvents (COSMO-RS). A good agreement was found between the model-estimated and experimental HGFs for the studied OS compounds. This quantum-chemistry-based approach for HGF estimation will open up the possibility of investigating the hygroscopicity of other OS compounds present in the atmosphere.

Improvement of tetracycline removal using amino acid ionic liquid modified magnetic biochar based on theoretical design

In current research, biochar is widely used to remove antibiotic contamination from the environment. However, original biochar produced through direct pyrolysis has significant limitations in removing antibiotics. In this paper, response surface methodology provided the laudable choice for magnetic poplar biochar (MPBC) considering the synergistic effect of multi-factors as well as the restrictions abundance experiments during the preparation of MPBC. Amino acid ionic liquids (AAILs) are a class of ionic liquids in which amino acids act as the anionic component, combining the tunable properties of ionic liquids with the biocompatibility and functionality of amino acids. To ameliorate adsorption capacity of MPBC, tetra-ethylammonium glycinate ([TEA][Gly]) with the best affinity for tetracycline (TC) were screened from 600 AAILs based on theoretical predictions using the conductor-like screening model for real solvents (COSMO-RS) without extensive experimental. Furthermore, AAILs-modified magnetic biochar (MPBC-AAIL) was synthesized chemically for the first time. Adsorption kinetics, isotherms, and thermodynamic analyses revealed that TC adsorption by both MPBC and MPBC-AAIL is a monolayer, spontaneous, and exothermic chemical process. Moreover, MPBC-AAIL exhibited a maximum adsorption capacity of 305.9 mg·g−1. Meanwhile, MPBC-AAIL maintains superior adsorption performance for TC in different impurity solutions as well as in real water samples. The excellent reusability and selective adsorption capacity of the material demonstrate the superiority of the theoretical based design. The characterization reveals that the adsorption mechanisms include pore filling, π-π interactions, surface complexation, hydrogen bonding, and electrostatic interactions. In addition, theoretical calculations showed that ionic liquid modification altered the adsorption sites between TC and MPBC-AAIL, leading to enhanced adsorption. This comprehensive study introduces an innovative modification of biochar, providing a novel approach to designing biochar composites and promoting their application in the remediation of complex environmental pollution.

Separation of ethanol/n-hexane azeotrope by imidazolium ionic liquids: Experimental study and mechanism analysis

In the process of producing low-carbon alcohols from coal, azeotropic waste containing ethanol and n-hexane is generated. To prevent environmental risks and resource waste from solvent loss, this study selected green solvent ionic liquids (ILs) for their separation. First, we screened five anions with high selectivity based on the conductor-like screening model for realistic solvents (COSMO-RS), and through various quantum chemical calculations, we found that the anions play a significant role in the separation of the two components, with [HSO4]− showing the best separation performance. Second, we predicted the phase equilibrium data for ILs containing [HSO4] − in the azeotropic system, considering practical factors such as price and viscosity, and selected [EMIM][HSO4], [BMIM][HSO4], and [HMIM][HSO4] for experiments. The results indicated that simpler cation side chains enhance the separation capability of the system. Then, a toxicity analysis of the ILs ensured their biocompatibility and feasibility. Finally, we established the extraction distillation process and visualized the interactions between the ILs and the azeotropic system through molecular dynamics simulations. This work provides theoretical guidance for the separation of alcohols and alkanes in the industry.

How does the integration of amino acids enhance the bonding ability of triazine-based inhibitors with iron surfaces: Insights from SCC-DFTB, COSMO-RS and molecular dynamics simulations

Theoretical investigations into the adsorption of molecules on metal surfaces play a pivotal role in the development of more efficient corrosion inhibitors. This study offers a novel approach by combining Density Functional Tight Binding (DFTB), Molecular Dynamics (MD) simulations, and conductor-like screening model for realistic solvation (COSMO-RS) calculations to comprehensively assess the interaction and diffusion properties of glycine (Gly), 2,2′-azanediyldiacetic acid (IDA), and 5-aminopentanoic acid (5-APA) with two 1,3,5-triazine (Tris) derivatives (Tris-Gly and Tris-IDA) for corrosion protection of carbon steel in acidic medium. The solvation properties of these molecules were evaluated alongside their interaction strength with the Fe(1 1 0) surface, considering both neutral and protonated forms. DFTB simulations revealed a clear trend in interaction energies, following the order: Tris-IDA > Tris-Gly > 5-APA > IDA > Gly. It ranges from −1.191 eV to −1.853 eV, with the highest stability observed for protonated Tris-IDA (−1.853 eV), while neutral Glycine exhibits the lowest interaction energy (−1.263 eV). While these theoretical results diverged slightly from experimental observations, with Tris-Gly outperforming Tris-IDA, this discrepancy was attributed to steric effects and solvent interactions. The DFTB results also indicated strong covalent interactions, particularly involving acetic groups, between the inhibitors’ orbitals and the iron’s 3d orbitals. MD simulations further demonstrated the impact of protonation on inhibitor mobility, with reduced diffusion coefficients due to enhanced electrostatic interactions and hydrogen bonding. COSMO-RS solvation analysis confirmed the strong hydrogen bonding capabilities of these molecules with water. Overall, this study elucidates the mechanisms of corrosion inhibition by integrating multiple simulation techniques, providing key molecular insights that can guide the design of more effective corrosion inhibitors. The findings emphasize the significance of electronic interactions and molecular structure in enhancing inhibitor performance, making this a valuable contribution to corrosion science.

Extraction of phenolic acids and tetramethylpyrazine in Shanxi aged vinegar base on vortex-assisted liquid-liquid microextraction-hydrophobic deep eutectic solvent: COSMO-RS calculations and ANN-GA optimization

In this study, vortex-assisted liquid-liquid microextraction (VA-LLME) based on hydrophobic deep eutectic solvents (HDES) was used to efficiently and sustainably extract five phenolic acids and tetramethylpyrazine (TMP) from Shanxi aged vinegar (SAV). The VA-LLME technique was employed to investigate the extraction mechanism of HDES with the best extraction performance for the target compounds using a conductor-like screening model for real solvents (COSMO-RS). An artificial neural network combined with a genetic algorithm (ANN-GA) was developed to optimize the extraction conditions based on single-factor and response surface methodology, while also analyzing the interactive effects on the phenolic acids and TMP in the extracted solution during the extraction phase. The optimized conditions were determined, and the greenness of the procedure was evaluated using an analytical greenness metric, indicating that this technique can serve as a green alternative for the determination of phenolic acids and TMP in SAV.

Phenolic Foam Preparation Using Hydrofluoroolefin Blowing Agents and the Toughening Effect of Polyethylene Glycol.

In this work, a new class of fourth-generation, zero ozone depletion potential, hydrofluoroolefin-based blowing agents were used to prepare phenolic foam. While hydrofluoroolefin blowing agents have been used previously to prepare polyurethane foams, few studies have been reported on their use in phenolic foams. We introduce an effective method for foam preparation using two low-boiling blowing agents, cis-1,1,1,4,4,4-hexafluoro-2-butene and trans-1,1,1,4,4,4-hexafluoro-2-butene, and their combinations with hexane. Traditionally, phenolic foams have been prepared using chlorofluorocarbons and hydrochlorofluorocarbons, which can have harmful effects on the environment due to their high ozone depletion potential or global warming potential. Conductor-like screening model for real solvents (COSMO-RS) modeling studies were performed to understand the effects of different blowing agent combinations on their boiling points. A series of phenolic foams were prepared by varying the concentration of the hydrofluoroolefin and the hydrofluoroolefin-hexane blowing agent combinations. The concentrations of the surfactant, Agnique CSO 30, and the toughening agent, polyethylene glycol, were also varied to yield a formulation with the optimal properties. The foams formulated with the hydrofluoroolefin-hexane mixture displayed a higher compressive strength and a lower thermal conductivity than those prepared with either hydrofluoroolefin or hexane alone. The cell microstructure of all the foams was examined using scanning electron microscopy. By introducing flexible chains into the resin matrix, PEG facilitates proper distribution of hydrofluoroolefin-hexane blowing agents and other reagents and thereby increases the mechanical strength of the foam.

Extraction of Pyrrole from Its Mixture with n-Hexadecane Using Protic Ionic Liquids.

The removal of nitrogen compounds from fuel via the conventional method, which is hydrodenitrogenation, is costly and involves catalysts and energy-intensive conditions (600 K and 300 atm). Recently, ionic liquids (ILs) have emerged as a promising alternative solvent for the denitrogenation of fuel oil. However, certain ILs are expensive and challenging to synthesize, prompting the exploration of protic ionic liquid (PIL) substitutes, which offer similar advantages to ILs. This study utilized the conductor-like screening model for real solvents (COSMO-RS) to predict the phase equilibria for three PILs-triethylammonium p-toluenesulfonate (TEA-TSA), triethylammonium salicylate (TEA-SA) and triethylammonium benzoate (TEA-BZ)-which were subsequently validated through experimental investigations. Liquid-liquid extraction experiments were conducted at 298 K and 1 atm, with pyrrole (serving as the model nitrogen compound) concentrations in n-hexadecane (representing the model fuel) ranging from 10 to 50 wt%. Additionally, the NRTL model effectively correlated the experimental tie lines. The obtained data indicated that TEA-TSA exhibited superior selectivity and distribution ratio compared to TEA-SA and TEA-BZ. All the ternary systems tested displayed positive slopes, suggesting a higher affinity of nitrogen compounds for the PIL. Supporting this observation, interaction energy (ΔE) and excess enthalpy (HE) were employed. The predicted outcomes revealed that TEA-TSA had high ΔE, and all PILs exhibited negative values of HE. The HE calculation underscored the significance of strong hydrogen bond interactions between pyrrole and the PIL for successful extraction.

Theoretical exploration and experimental investigation of acidic deep eutectic solvents as green solvents to separate carbazole from model crude anthracene oil

Carbazole is a highly value-added compound derived from crude anthracene. Deep eutectic solvents (DESs) have been extensively used as promising green solvents for the separation of target compounds from mixture. In this work, 135 acidic DESs, formed by combining 9 quaternary ammonium salts with 15 common acids, were screened based on the conductor-like screening model for real solvents (COSMO-RS) model. The four top-ranked DESs, including tetraethylammonium chloride (TEAC)/2 acetic acid (AA), TEAC/2 propionic acid (PA), tetrapropylammonium chloride (TPAC)/2AA, and TPAC/2PA were identified as suitable extractants for carbazole separation based on their performance index (PI). Among them, TEAC/2AA had the best separation performance for carbazole due to its highest PI value (1448.19). Liquid–liquid extraction experiments were performed, and the results were in good accordance with those predicted by COSMO-RS calculations. Therefore, TEAC/2AA was determined as the most promising DES for the carbazole separation. The extraction conditions of TEAC/2AA were optimized. The extraction efficiency (EE) and selectivity (SE) of carbazole were 97.67 % and 66.64 %, respectively, under the optimal extraction conditions (25 ℃, 5 min of extraction time, DES-oil mass ratio of 1:5, initial carbazole concentration of 5000 ppm). Additionally, the deionized water was employed to regenerate the DES and recovery of carbazole. The recycling performance of promising DES was also investigated. Finally, quantum chemical calculations and Fourier Transform Infrared spectroscopy (FT-IR) analysis were conducted to explore the separation mechanism. The results indicated that the separation of carbazole by DESs were predominantly dominated by hydrogen bonds and van der Waals interaction. This work provided a valuable reference for the selection and design of DES extractants to separate other high value-added components from mixture.

Application of terpenoids for the remediation of environmental water polluted with bisphenol A and its analogs using an in silico approach

Nowadays, there is a global concern over water quality and the impact of contamination on both natural ecosystems and human well-being. Plastics, ubiquitous in modern life, may release harmful chemicals when they reach aquatic environments. Among them, bisphenol A (BPA) and its alternatives, such as bisphenol S (BPS), bisphenol F (BPF), and others, are of special concern because their presence in water systems can have detrimental effects on human health and aquatic organisms due to their endocrine-disrupting properties.This study explores the potential of terpenoids, sustainable and environmentally friendly solvents, for efficiently removing bisphenols from contaminated environmental water. Using an in silico approach based on the Conductor-like Screening Model for Realistic Solvents (COSMO-RS) theory, more than 30 terpenoids were screened, and carvone was found to be an excellent candidate due to its high solvent capacity and low toxicity. The impact of pH, temperature, stirring conditions, and sample:extractant phase ratios on the extraction efficiency were investigated. A design of experiments revealed optimal conditions for the extraction process and demonstrated that carvone can effectively extract bisphenols (nearly 100 % for most of them) under a wide range of conditions, showing the robustness and efficiency of the extraction method, even in environmental samples.The paper provides valuable insights into the potential of terpenoids, specifically carvone, as a sustainable and eco-friendly solvent for removing bisphenol contaminants from environmental water bodies. The findings of this study offer a promising solution to address water contamination issues, aligning with the principles of Green Chemistry and contributing to a more environmentally responsible approach to water remediation.

Drug–polymer compatibility prediction via COSMO-RS

In this work, the solid–liquid equilibrium (SLE) curve for ten active pharmaceutical ingredients (APIs) with the polymer polyvinylpyrrolidone (PVP) K12 was purely predicted using the Conductor-like Screening Model for Real Solvents (COSMO-RS). In particular, two COSMO-RS-based strategies were followed (i.e., a traditional approach and an expedited approach), and their performances were compared. The veracity of the predicted SLE curves was assessed via a comparison with their respective SLE dataset that was obtained using the step-wise dissolution (S-WD) method. Overall, the COSMO-RS-based API–PVP K12 SLE curves were in satisfactory agreement with the S-WD-based data points. Of the twenty predicted SLE curves, only two were found to be in strong disagreement with the corresponding experimental values (both modeled using the expedited approach). Hence, it was recommended to use the traditional approach when predicting the API–polymer SLE curve. At the present moment, COSMO-RS may be an effective computational tool for the expeditious screening of API–polymer compatibility, particularly in the case of promising novel APIs, for which experimental datasets are likely limited or non-existent.

Machine learning models coupled with ionic fragment σ-profiles to predict ammonia solubility in ionic liquids

Emitting NH3 into the atmosphere leads to significant air pollution, while NH3 itself serves as an essential component for fertilizers and refrigerants in industry. Thus, recovering and reusing NH3 is highly valuable. Ionic liquids (ILs) have shown great potential for NH3 capture, where the accurate prediction of solubility is a critical point for selecting ILs and designing a separation process. This work combined the Ionic Fragment Contribution (IFC) strategy with machine learning (ML) to develop four models (IFC-ML) to predict NH3 solubility in ILs. A dataset containing 785 solubility data points, covering 10 cations and 10 anions, was collected. From this dataset, the S1–S6 descriptors based on the IFC method were used as inputs for the ML models, together with temperature (T) and pressure (P). Among the models, the IFC-GBR model was recommended for predicting NH3 solubility in ILs due to its higher coefficient of determination (R2) of 0.9945 and lower mean squared error (MSE) of 0.0003 than the others. Additionally, in comparison with previous conductor-like screening model for real solvents (COSMO-RS) and extreme learning machine (ELM) methods, the IFC-GBR method showed a more accurate prediction of the NH3 solubility in ILs over a wider range of temperatures and pressures, providing additional chemical insights into IL-NH3 system that cations played a more important role for NH3 solubility. These results highlighted the developed IFC-GBR (gradient boosting regressor) model offered valuable insights for helping guide the process design of absorbing NH3 through IL-based technology.

Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

Estimation of interfacial tension (IFT) and % reduction in IFT of Brazil and China regions’ heavy crude oil is employed by Artificial Intelligence (AI) led frameworks. The uniqueness of proposed approach lies in using AI frameworks that are constructed using Artificial Neural Network (ANN) and meta-heuristic algorithms i.e. Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) forms three AI frameworks namely ANN, ANN-GA, and ANN-GWO is employed for IFT estimation of heavy oil and deep eutectic solvent-brine interface. The hyper-parameters of the frameworks are trained, validated, and tested over 18228 IFT data points obtained from the Omani heavy crude oil. The intermolecular interactions between oil, brine, and DES are obtained by generating surface segments in COnductor like Screening MOdel for Real Solvent (COSMO-RS) framework. The composition of brine, mole ratio of oil, DES, and temperature are other input parameters for AI model. The IFT estimation leads to the selection of 3 hydrophilic DESs capable of greater than 75 % IFT reduction and 14 hydrophilic DESs capable of 50–75 % IFT reduction. ANN-GWO scores best AI framework among 3 whereas ANN-GA fails to estimate IFT reduction greater than 50 %. Analysis of IFT with respect to mole fraction reveals the most suitable range of DES addition for IFT reduction.

A combined theoretical and experimental investigation on the solvatochromism of fused chromene-furanopyran: Determination of dipole moments, DFT/TD-DFT, chemical reactivity and Fukui Function

This work focuses on the steady-state optical absorption and fluorescence spectra of three amino aryl-4H-furo[3,2-c]pyran -4H-chromene-3-carbonitrile derivatives. The ground state (µg) and excited state (µe) dipole moments were determined by the solvatochromic shift method using Kawski-Bilot and Ravi et al. equations. The excited-state dipole moment was found to be larger than that of the ground-state. The measured ground and excited state dipole moments as well as the different photophysical parameters do adequately reflect the results obtained from the computational methods.Frontier Molecular Orbitals (FMOs) analysis, the Fukui indices, COSMO-RS exploration, and Hirschfield population analysis were investigated. In order to gain a deeper understanding of the source of reactivity in the three molecules, the COnductor-like Screening MOdel for Real Solvents (COSMO-RS) was used. It is important to note that the COSMO-RS analysis of three chromenes is in complete agreement with the Hirschfield population analysis.

Molecular simulation-based assessing of a novel metal-organic framework modified with alginate and chitosan biopolymers for anionic reactive black 5 and cationic crystal violet pollutants capture

This study employed molecular simulations to develop efficient and environmentally friendly adsorbents for removing two common dye pollutants, crystal violet (CRVT) and reactive black 5 (RBC5). The investigated adsorbents are based on biodegradable polymers alginate (ALG) and chitosan (CHAN) functionalized with carboxyl acid groups (CA) on a UIO-66 metal–organic framework (MOF). Density functional theory (DFT) analyses such as electrostatics potential (ESP), Average local ionization energy (ALIE) analysis, COSMO-RS (Conductor-like Screening Model for Real Solvents) analysis and the density of states (DOS) were performed to investigate the structures of the pollutants and adsorbents. Physical and chemical properties of the adsorbents, including density, Hamiltonian energy (HAE), the radius of gyration (ROG), and the stress autocorrelation function (SACF) were also examined. Additionally, molecular dynamics (MD) and Monte Carlo (MC) simulations were employed to study the adsorption of CRVT and RBC5 onto CA/UIO-66, CA/UIO-66/ALG, and CA/UIO-66/CHAN adsorbents. The results demonstrated that functionalizing CA/UIO-66 with CHAN and ALG significantly enhances the adsorption capacity for both CRVT and RBC5. The modified adsorbent demonstrated significantly higher affinity towards RBC5 compared to CRVT, as evidenced by the adsorption energies of −1.11 × 104 kcal/mol for CA/UIO-66-CHAN-W-CRVT and −1.36 × 107 kcal/mol for CA/UIO-66-CHAN-W-RBC5. This research highlights the potential of CHAN- and ALG-functionalized CA/UIO-66-based adsorbents as promising alternatives to current wastewater treatment methods. These adsorbents are efficient, biodegradable, and environmentally friendly, offering remarkable potential for removing dye pollutants from industrial and domestic wastewater.

Prediction on Air-Nasal Mucus Partition Coefficients of Odor Compounds.

The air-nasal mucus partition coefficient is a crucial property among all of the interaction mechanisms between odor molecules and olfactory receptors, since this property contributes to our sense of smell. Due to the complexity of the mucus composition, in vivo determination of the air-mucus partition coefficient is a technical challenge. A predictable model of the air-mucus partition coefficient can provide valuable insights into the chemical properties that govern olfactory perception and can help design desired odorants. In this study, we propose a novel model based on the deep-layer neural network (DNN) algorithm to predict the air-mucus partition coefficients for a range of odor compounds. The molecular surface charge density (σ-profile) calculated from the COnductor like Screening MOdel for Real Solvents (COSMO-RS) thermodynamic package was adapted as descriptors of structural features of odor molecules. The results revealed that the air-mucus partition coefficients are highly correlated to the σ-profile of the studied compounds. The information obtained from the study provided interpretable results, which not only help in identifying the molecular features that contribute to the air-mucus partition coefficient of odorants but also aid in the design of compounds with the desired odor properties.

Screening of ionic liquids for the solubility enhancement of quinine using COSMO-RS

Quinine has traditionally been used in medicine to treat parasitic disease malaria due to its antimalarial activities. It is sparingly soluble in water and highly soluble in organic solvents. However, these solvents are not environmentally friendly, so greener solvents need to be explored and designed. Ionic liquids (ILs) were successfully observed to be greener and capable of dissolving biologically active compounds. However, finding a potential IL from millions of possible IL combinations is challenging. This work utilizes the conductor-like screening model for real solvents (COSMO-RS) to address this issue. This study aims to determine the dissolution behavior of quinine in ILs employing the COSMO-RS. A total of 540 IL combinations of 9 cations and 60 anions were investigated. The efficacy of each IL was evaluated by predicting the activity coefficients at infinite dilution, solvation-free energy, selectivity, capacity, and performance index (P.I.). This study reveals that ILs with shorter alkyl chains, polar and non-aromatic characteristics, and smaller anions are advantageous for the solubility enhancement ofquinine.The most effective ILs, choline acetate, choline glycinate, and choline threoninate, displayed the highest selectivity, capacity, P.I., and lowest activity coefficient and solvation-free energies. The findings of the current study unlock key insights into the solubility behaviour of quinine in an aqueous solution employing ILs.

Machine learning approach to predict Hansen solubility parameters of cocrystal coformers via integrating group contribution and COSMO-RS

Hansen Solubility Parameters (HSPs) have been a hot topic on predicting the tendency of pharmaceutical cocrystals formation and cocrystal coformers (CCFs) screening. However, the limitation of such application is the lack of models to accurately predict the values of HSPs for drug CCFs with more structural complexity. Accordingly, three ML (machine learning) models, i.e. ANN (Artificial Neural Network), XGBoostRegressor (Extreme Gradient Boosting Regressor) and LGBMRegressor (Light Gradient Boosting Machine Regressor), were developed for predicting the HSPs on CCFs screening for drugs. The HSPs database for 181 CCFs (containing alcohols, alkenes, aromatics, haloalkanes, amines, ketones, ethers, amides, esters, pharmaceuticals, alkanes, acids, nitroalkanes) were established and classified into the training set (140 compounds) and the test set (41 compounds with various functional polarity and groups, covering solid reagents and solvents). The prediction molecular descriptors were combined from the GC (Group Contribution) methods, the COSMO-RS (the Conductor-like Screening Model for Real Solvents) sigma-moments and energy descriptors. The results showed that ANN and XGBoostRegressor beat out LGBMRegressor in predicting HSPs for CCFs. Finally, SHapley Additive exPlanations (SHAP) was employed to visualize and explain the most important characteristics and effects on predicting HSPs via XGBoostRegressor, indicating that CH3, M2 and MHbdon3 had a significant influence and high contribution to the prediction of δd, δp and δh, respectively. The coupled GC and COSMO-RS strategy had been proven as a promising tool to predict HSPs through XGBoostRegressor for screening, designing, and selecting CCFs for drugs.

Evaluating asphaltene dispersion with choline chloride or menthol based deep eutectic solvents: A COSMO-RS analysis

Deep Eutectic Solvents (DESs) have emerged as a distinct class of solvents with unique properties and advantages over conventional Ionic Liquids (ILs) and organic solvents. This study specifically focuses on evaluating the potential of DESs as aggregation inhibitors for asphaltenes. Utilizing the Conductor-like Screening Model for Real Solvent (COSMO-RS) method, we investigated a series of DESs based on choline chloride and menthol. The COSMO-RS approach enabled the accurate prediction of key thermodynamic properties, including density, σ profiles (charge distribution), and σ potentials (electrostatic potential) of the DESs. Our findings identified eight DESs as particularly effective in dispersing asphaltenes, providing a promising foundation for further experimental validation and potential application in the petroleum industry.