Prediction Of Solubility Research Articles

Rational solvent selection for the preparation of industrial monolithic supported liquid-phase (SLP) olefin hydroformylation catalyst

Heterogenisation of the hydroformylation (HyFo) process converting C4 olefins and syngas (CO/H2) to n-pentanal can substantially reduce its carbon footprint by simplifying downstream processing. Current upscaling efforts include constructing a pilot plant containing a Rh-diphosphite-stabilized supported liquid-phase (SLP) catalyst. The air-sensitive nature of the catalyst requires in-situ impregnation of the porous support material with the liquid phase components (Rh-precursor, ligand, stabiliser) entailing the use of large quantities of an organic solvent. Environment, health, and safety (EHS) as well as regulatory aspects prompt the search for an alternative to dichloromethane (DCM), which was used as standard solvent for lab-scale studies. To obtain alternatives to DCM, the replacement solvent methyl tert-butyl ether (MTBE) and in-silico screening of more than 5000 solvent candidates has been performed covering boiling points and solubility predictions by COSMO-RS as well as carcinogenicity- and mutagenicity predictions by quantitative structure–activity relationship (QSAR) models. The final list of solvent candidates contained unsaturated compounds (alkenes, alkynes) in addition to ether- and ester-functionalized​ solvents, which are typically recommended as DCM replacements. Sufficiently high solubilities of ligand and stabiliser to prepare the desired SLP catalyst were experimentally determined in the following solvents: methyl propionate (MP), dimethoxymethane (DMM), MTBE, tetrahydrofuran (THF) and DCM for benchmarking, and a monolithic SLP catalyst prepared with MTBE demonstrated to perform equally well in the HyFo reaction of 1-butene as the benchmark catalyst prepared using DCM solvent. Overall, the study demonstrates a practical approach for selecting solvents with a greener profile for making catalyst systems applicable for large-scale production.

Machine learning methods for predicting CO2 solubility in hydrocarbons

The application of carbon dioxide (CO2) in enhanced oil recovery (EOR) has increased significantly, in which CO2 solubility in oil is a key parameter in predicting CO2 flooding performance. Hydrocarbons are the major constituents of oil, thus the focus of this work lies in investigating the solubility of CO2 in hydrocarbons. However, current experimental measurements are time-consuming, and equations of state can be computationally complex. To address these challenges, we developed an artificial intelligence-based model to predict the solubility of CO2 in hydrocarbons under varying conditions of temperature, pressure, molecular weight, and density. Using experimental data from previous studies, we trained and predicted the solubility using four machine learning models: support vector regression (SVR), extreme gradient boosting (XGBoost), random forest (RF), and multilayer perceptron (MLP). Among four models, the XGBoost model has the best predictive performance, with an R2 of 0.9838. Additionally, sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO2 solubility in hydrocarbons is most sensitive to pressure. Furthermore, our trained model was compared with existing models, demonstrating higher accuracy and applicability of our model. The developed machine learning-based model provides a more efficient and accurate approach for predicting CO2 solubility in hydrocarbons, which may contribute to the advancement of CO2-related applications in the petroleum industry.

CO2 solubility and amine volatility data for low-concentration solutions of MEA, AMP, PZ and CESAR-1 blend (AMP/PZ)

Water-wash section(s) effectively reduce amine compound emissions in amine-based carbon capture processes. Reliable predictions of the behavior of these emission control technologies depend on detailed understanding of the vapor-liquid equilibrium (VLE) of water wash system. However, existing vapor-liquid equilibrium data in the literature focus primarily on solvent-like amine concentrations in amine–CO2–H2O systems; hence, new low-concentration VLE data are needed. In this work, the CO2 solubility in low-concentration solutions of monoethanolamine (MEA), piperazine (PZ), 2-amino-2-methyl-1-propanol (AMP), and AMP/PZ blend (CESAR-1) was investigated for temperatures between 303 and 353 K at atmospheric pressure. Additionally, amine volatility measurements for CO2-loaded low-concentration solutions of MEA, PZ, AMP and CESAR-1 were performed in the range of 313–373 K and 8–96 kPa. Overall, the data show that the CO2 partial pressure increases with increasing CO2 loading and temperature. Similarly, the amine partial pressure increases with increasing pressure and temperature. The new experimental data were compared with two existing VLE models using electrolyte non-random two-liquid activity coefficient models to evaluate the ability of the latter to predict vapor-liquid equilibrium behavior. Both CO2 solubility and amine volatility predictions by the models tend to show significant deviations. The comparison highlight the need for such low-concentration data for all systems studied. This data shall serve to improve existing water-wash modeling tools.

Computer-Aided Drug Design (CADD): Pinostrobin and Chitosan Combination as an Antimicrobial Agent

Antimicrobial resistance causes a decrease in the effectiveness of drugs for infection therapy. CAAD can be a solution to predict solubility through chemical reactivity and interaction visibility type for new antimicrobial agents utilizing computers. The purpose of the research project was to estimate the combination of pinostrobin and chitosan's solubility and antibacterial activity. ChemBio Draw software was used to determine chemical reactivity. The solubility prediction was computed by the AutoDock Tool software. The antimicrobial activity was measured by the MOE software. Pyruvate kinase (PDB ID: 3T07), MRSA PBP2a (PDB ID: 4DKI), UPPS (PDB ID: 4H8E), and DHFR (PDB ID: 2W9S) were chosen as target proteins in the study. Pinostrobin had I, A, χ, δ, and ω values higher than those of chitosan, except ΔE, η, and μ. The interaction visibility type and bond distance between pinostrobin and chitosan, such as two conventional hydrogens (2.16-2.23 Å) and three hydrophobic pi-sigma (3.10-3.91 Å). The free energy binding value of pinostrobin, which interacted with proteins including 3T07, 4DKI, 4H8E, and 2W9S, respectively, was -6.0109, -5.4356, -6.4202, and -6.5748 kcal/mol. Chitosan with the same proteins had -11.4987, -111.1761, -11.1157, and -11.0369 kcal/mol. Pinostrobin is more stable and has lower reactivity than chitosan. The process of pinostrobin and chitosan solubility in each other was significantly impacted by the hydrogen and hydrophobic pi-sigma bonds. The antimicrobial prediction of chitosan was higher than that of pinostrobin against all target proteins. This research was the basis for isolating pinostrobin and developing a drug delivery system combined with chitosan.

Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents.

Carbon dioxide (CO2) is a detrimental greenhouse gas and is the main contributor to global warming. In addressing this environmental challenge, a promising approach emerges through the utilization of deep eutectic solvents (DESs) as an ecofriendly and sustainable medium for effective CO2 capture. Chemically reactive DESs, which form chemical bonds with the CO2, are superior to nonreactive, physically based DESs for CO2 absorption. However, there are no accurate computational models that provide accurate predictions of the CO2 solubility in chemically reactive DESs. Here, we develop machine learning (ML) models to predict the solubility of CO2 in chemically reactive DESs. As training data, we collected 214 data points for the CO2 solubility in 149 different chemically reactive DESs at different temperatures, pressures, and DES molar ratios from published work. The physics-driven input features for the ML models include σ-profile descriptors that quantify the relative probability of a molecular surface segment having a certain screening charge density and were calculated with the first-principle quantum chemical method COSMO-RS. We show here that, although COSMO-RS does not explicitly calculate chemical reaction profiles, the COSMO-RS-derived σ-profile features can be used to predict bond formation. Of the models trained, an artificial neural network (ANN) provides the most accurate CO2 solubility prediction with an average absolute relative deviation of 2.94% on the testing sets. Overall, this work provides ML models that can predict CO2 solubility precisely and thus accelerate the design and application of chemically reactive DESs.

Characteristics and Challenges of Poly(ethylene-co-vinyl acetate) Solution Electrospinning.

Poly(ethylene-co-vinyl acetate) (PEVA) is a versatile elastic, durable, and biocompatible copolymer, which can be processed by melt extrusion or solvent casting, while electrospinning has been reported as challenging. Here, a spinnability window should be identified using a total of 10 different PEVA materials with increasing vinyl acetate content (∼12-40 wt %) and molecular weights (∼60-130 kDa). Based on the solubility predictions by calculating Hansen solubility parameters, candidate solvents were experimentally evaluated. Spinning experiments with systematic alteration of solution composition and processing parameters revealed the causes of material deposition at the spraying nozzle and multijet spinning characteristics. By introducing a spinnability score that accounts for product characteristics and reproducibility, the spinnability of PEVA could be rationalized. Overall, it was demonstrated that PEVA solutions with an apparent viscosity of 920-3500 mPa·s can be spun to bead-free fibers of ∼10 μm. This size may allow suspension electrospinning of composite fibers in the future.

Measurement and prediction of CO2 solubility in organic electrolytes for high pressure CO2 reduction

The electrochemical CO2 reduction reaction (CO2RR) to organic products is becoming increasingly important. Here, organic electrolytes play a crucial role, since adding salt to organic solvent allows an increase in the current density, and together with supercritical CO2 organic electrolytes suppress hydrogen evolution during the CO2RR. Since the selectivity to organic products depends on the CO2 content, CO2 solubility was investigated in organic electrolytes at different salt concentrations, pressures (up to 150 bar), and temperatures (25 °C and 70 °C). These organic electrolytes included the mixtures NaI-methanol, KSCN-methanol, (C2H5)4NBF4-acetonitrile, and (C4H9)4NBF4-acetonitrile. Afterwards, electrolyte Perturbed-Chain Statistical Associating Fluid Theory (ePC-SAFT) was applied to predict the CO2 solubility in these organic electrolytes. The results showed that i) CO2 solubility decreased with increasing temperature and salt concentration, ii) dissolving CO2 may cause a precipitation of salts, and that iii) ePC-SAFT allowed to qualitatively model the CO2 solubility in organic electrolytes

Employment of artificial intelligence approach for optimizing the solubility of drug in the supercritical CO2 system

This paper investigates the solubility behavior of digitoxin in supercritical carbon dioxide (CO2) through a comprehensive analysis employing ensemble learning techniques and various regression models. The dataset consists of temperature and pressure as input variables, with solvent density and digitoxin solubility as output variables. Utilizing bagging as the ensemble method, Gaussian process regression (GPR), Bayesian Ridge Regression (BRR), Orthogonal Matching Pursuit (OMP), and Polynomial Regression (PR) were employed as regression models. Hyper-parameter tuning was achieved through gradient-based optimization. Results revealed that Polynomial Regression (PR) emerges as the most effective model for predicting both solvent density and digitoxin solubility. For solubility prediction, BAG-PR yields an R2 score of 0.98527, with MSE and MAE of 1.4290E-03 and 3.40547E-02, respectively. Concerning solvent density, BAG-PR achieves an outstanding R2 of 0.99766, along with MSE and MAE of 7.4759E+01 and 7.33964E+00, respectively. These results show that ensemble learning, and polynomial regression can accurately predict solubility and solvent density, revealing digitoxin's behavior in supercritical CO2.

Experimental and molecular simulation analysis of the dissolution behavior of Musk ambrette in organic solvents

The solubility of Musk ambrette in i-propanol (IPA), i-butanol (IBA), n-heptane (HPT), n-hexane (HX), cyclohexane (CYH), methyl acetate (MAC), ethyl acetate (EAC), acetone (ACE), IPA + ACE at 278.15 K to 318.15 K was determined. The mole fraction solubility of Musk ambrette in pure IPA at the temperature of 298.15 K were found to be 0.9492 × 10-2 mol·mol−1. The difference is that the solubility of Musk ambrette in ACE reaches 0.2257 mol·mol−1, which is much higher than the solvent mentioned above. The strong intermolecular hydrogen bonding formed by IPA resulted in its lowest solvation capacity for Musk ambrette. In addition, to further investigate the solubility pattern of Musk ambrette, we used 8 models (Van’t Hoff, modified Apelblat, Buchowski-Ksiazczak, Yaws, NRTL, Wilson, Jouyban-Acree and Sun model) to fit and obtain a general equation for solubility versus temperature. These results yielded a large amount of statistically significant data. Comparisons revealed that the modified Apelblat and Yaws equation showed the least deviation in forecasting solubility variations with temperature. Meanwhile, we proved that the dissolution of Musk ambrette is a heat-absorbing entropy increasing process by Van't Hoff equation calculation. ΔsolGo for the 12 solvents is in the following order: ACE < MAC < EAC < w = 0.2007 < w = 0.4025 < w = 0.6076 < CYH < w = 0.8070 < HPT < HX < IBA < IPA. ΔsolGo is smaller the easier it dissolves, which is consistent with the law of dissolution. Secondly, we performed thermal analysis and X-ray diffraction tests on Musk ambrette crystals prepared in different solvents, which showed that none of the several systems investigated led to the creation of new crystalline forms or solvatoids, further confirming the suitability of the solvents. Besides, we discuss the correspondence between solubility and the HSP, π, α, β, and CED of the solvents and thus tentatively confirm the significant influence of the solvent properties on solubility. The principle of similarity and compatibility is satisfied for most of the solvents. In particular, the CED values of the larger solvents were not favorable for the solubilization of Musk ambrette. We also analyzed the molecular surface electrostatic potential of Musk ambrette by visualization techniques. Finally, IPA and ACE and their binary solution systems were analyzed by molecular dynamics simulations. The effect of solvent intermolecular interactions on the solubilization pattern was discussed. It was further confirmed that weak interactions of solvent molecules contribute more to the dissolution of Musk ambrette. These findings suggest that the prediction of solvent solubility can be initially achieved by molecular simulation techniques. Undoubtedly, this study will furnish a valuable information for the purification of Musk ambrette.

Drug Solubility Prediction: A Comparative Analysis of GNN, MLP, and Traditional Machine Learning Algorithms

The effective development and design of pharmaceuticals hold fundamental importance in the fields of medicine and the pharmaceutical industry. In this process, the accurate prediction of drug molecule solubility is a critical factor influencing the bioavailability, pharmacokinetics, and toxicity of drugs. Traditionally, mathematical equations based on chemical and physical properties have been used for drug solubility prediction. However, in recent years, with the advancement of artificial intelligence and machine learning techniques, new approaches have been developed in this field. This study evaluated different modeling approaches consisting of Graph Neural Networks (GNN), Multilayer Perceptron (MLP), and traditional Machine Learning (ML) algorithms. The Random Forest (RF) model stands out as the optimal performer, manifesting superior efficacy through the attainment of minimal error rates. It attains a Root Mean Square Error (RMSE) value of 1.2145, a Mean Absolute Error (MAE) value of 0.9221, and an R-squared (R2) value of 0.6575. In contrast, GNN model displays comparatively suboptimal performance, as evidenced by an RMSE value of 1.8389, an MAE value of 1.4684, and an R2 value of 0.2147. These values suggest that the predictions of this model contain higher errors compared to other models, and its explanatory power is lower. These findings highlight the performance differences among different modeling approaches in drug solubility prediction. The RF model is shown to be more effective than other methods, while the GNN model performs less effectively. This information provides valuable insights into which model should be preferred in pharmaceutical design and development processes.

Abstract 3534: GeneLLM: Unveiling gene functions through literature-driven transformer embeddings

Abstract INTRODUCTION: Our understanding of gene properties has advanced through representation learning such as Alpha fold. Representation learning involves encoding the relationships between genes by embedding them into a numerical space. These embeddings, which capture complex genetic interactions and characteristics, can then be leveraged by machine learning models to predict various gene properties. Current embeddings derive from transcriptome or sequence data. Over the past 150 years, numerous experimental assays have uncovered gene functions and interactions- comprehensive knowledge documented in the literature but not always evident in transcriptome or sequence data. It has been posited that leveraging this knowledge to create gene embeddings; however, could result in machine learning models biased towards well-studied genes. METHODS AND RESULTS: We tested this hypothesis by developing a novel knowledge-embedding framework, GeneLLM. During training, GeneLLM learns to comprehend summaries of every gene- a compressed form of published knowledge- using Large Language Models (LLMs), fine-tuned for downstream tasks mapping cellular properties and biochemical processes. Despite the expected bias towards well-known genes, GeneLLM surprisingly showed high predictive power for an array of gene properties. Compared to baseline models, GeneLLM boosted an increase in performance of 20.3% correlation in gene conservation across species, 8.6% and 57.2% prediction accuracy in subcellular localization and gene ontology respectively. GeneLLM also showed competitive results on solubility prediction with 0.91 accuracy and a correlation of 0.71 for tissue-specific expression levels for 1001 cell lines. We also showed that the bias toward well-known genes could be mitigated by combining GeneLLM representation with transcriptome or sequence-based embedding. The combined embeddings exhibited superior performance to their individual components which suggests that GeneLLM extracts views complementary to existing embedding methods. CONCLUSION: The GeneLLM framework demonstrates the ability of LLMs to extract information from the rich knowledge available about the nexus of genes and their cellular traits. It also illustrates how bias in knowledge representation is complementary to other transcriptome and sequence-based information. This ability of GeneLLM to advance our understanding of genes, their roles in cellular processes, and their impact on oncogenesis, as well as in response and resistance mechanisms, highlights its potential in cancer research. Citation Format: Ala Jararweh, Kushal Virupakshappa, Oladimeji S. Macaulay, Aaron Segura, Olufunmilola M. Oyebamiji, Yue Hu, Avinash D. Sahu. GeneLLM: Unveiling gene functions through literature-driven transformer embeddings [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3534.

Machine learning prediction of methane, ethane, and propane solubility in pure water and electrolyte solutions: Implications for stray gas migration modeling

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs. A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers. The stray gas can dissolve in groundwater leading to chemical and biological reactions, which could negatively affect groundwater quality and contribute to atmospheric emissions. The knowledge of light hydrocarbon solubility in the aqueous environment is essential for the numerical modelling of flow and transport in the subsurface. Herein, we compiled a database containing 2129 experimental data of methane, ethane, and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure. Two machine learning algorithms, namely regression tree (RT) and boosted regression tree (BRT) tuned with a Bayesian optimization algorithm (BO) were employed to determine the solubility of gases. The predictions were compared with the experimental data as well as four well-established thermodynamic models. Our analysis shows that the BRT-BO is sufficiently accurate, and the predicted values agree well with those obtained from the thermodynamic models. The coefficient of determination (R2) between experimental and predicted values is 0.99 and the mean squared error (MSE) is 9.97 × 10−8. The leverage statistical approach further confirmed the validity of the model developed.

Will we ever be able to accurately predict solubility?

Accurate prediction of thermodynamic solubility by machine learning remains a challenge. Recent models often display good performances, but their reliability may be deceiving when used prospectively. This study investigates the origins of these discrepancies, following three directions: a historical perspective, an analysis of the aqueous solubility dataverse and data quality. We investigated over 20 years of published solubility datasets and models, highlighting overlooked datasets and the overlaps between popular sets. We benchmarked recently published models on a novel curated solubility dataset and report poor performances. We also propose a workflow to cure aqueous solubility data aiming at producing useful models for bench chemist. Our results demonstrate that some state-of-the-art models are not ready for public usage because they lack a well-defined applicability domain and overlook historical data sources. We report the impact of factors influencing the utility of the models: interlaboratory standard deviation, ionic state of the solute and data sources. The herein obtained models, and quality-assessed datasets are publicly available.

Roles of pH and phosphate in rare earth element biosorption with living acidophilic microalgae

The increasing demand for rare earth elements (REEs) has spurred interest in the development of recovery methods from aqueous waste streams. Acidophilic microalgae have gained attention for REE biosorption as they can withstand high concentrations of transition metals and do not require added organic carbon to grow, potentially allowing simultaneous sorption and self-replication of the sorbent. Here, we assessed the potential of Galdieria sulphuraria for REE biosorption under acidic, nutrient-replete conditions from solutions containing ≤ 15 ppm REEs. Sorption at pH 1.5–2.5 (the growth optimum of G. sulphuraria) was poor but improved up to 24-fold at pH 5.0 in phosphate-free conditions. Metabolic activity had a negative impact on REE sorption, additionally challenging the feasibility of REE biosorption under ideal growth conditions for acidophiles. We further examined the possibility of REE biosorption in the presence of phosphate for biomass growth at elevated pH (pH ≥ 2.5) by assessing aqueous La concentrations in various culture media. Three days after adding La into the media, dissolved La concentrations were up to three orders of magnitude higher than solubility predictions due to supersaturation, though LaPO4 precipitation occurred under all conditions when seed was added. We concluded that biosorption should occur separately from biomass growth to avoid REE phosphate precipitation. Furthermore, we demonstrated the importance of proper control experiments in biosorption studies to assess potential interactions between REEs and matrix ions such as phosphates.Key points• REE biosorption with G. sulphuraria increases significantly when raising pH to 5• Phosphate for biosorbent growth has to be supplied separately from biosorption• Biosorption studies have to assess potential matrix effects on REE behavior

A predictive model and experimental validation for the compatibility of poly(xylitol succinate) with common solvents

Poly(xylitol succinate) was synthesized from xylitol and succinic acid with a molar ratio ranging from 1:1 to 1:3. The produced materials were purified and characterized by infrared and mass spectrometry. Theoretical calculations of the Hansen solubility parameters were performed for two proposed models, liquid and solid polymers by using the group contribution methods of Hoftyzer-van Krevelen. While liquid and solid polymers were compatible with polar solvents, such as water, ethanol and methanol, non-polar solvents showed poor compatibility with polymers. Experimental solubility tests were conducted, and the results were consistent with the predicted compatibility. This methodology enables not only the prediction of polymeric material solubility, but also facilitates the selection of optimum solvents for purification processes, which is a critical step prior to industrial applications.

Experimental analysis and prediction of radionuclide solubility using machine learning models: Effects of organic complexing agents

Radioactive wastes contain organic complexing agents that can form complexes with radionuclides and enhance the solubility of these radionuclides, increasing the mobility of radionuclides over great distances from a radioactive waste repository. In this study, four radionuclides (cobalt, strontium, iodine, and uranium) and three organic complexing agents (ethylenediaminetetraacetic acid, nitrilotriacetic acid, and iso-saccharic acid) were selected, and the solubility of these radionuclides was assessed under realistic environmental conditions such as different pHs (7, 9, 11, and 13), temperatures (10 °C, 20 °C, and 40 °C), and organic complexing agent concentrations (10−5–10−2 M). A total of 720 datasets were generated from solubility batch experiments. Four supervised machine learning models such as the Gaussian process regression (GPR), ensemble-boosted trees, artificial neural networks, and support vector machine were developed for predicting the radionuclide solubility. Each ML model was optimized using Bayesian optimization algorithm. The GPR evolved as a robust model that provided accurate predictions within the underlying solubility patterns by capturing the intricate relationships of the independent parameters of the dataset. At an uncertainty level of 95%, both the experimental results and GPR simulated estimations were closely correlated, confirming the suitability of the GPR model for future explorations.

Allantoin solubility in high pressure CO2: Statistical correlation and thermodynamic modeling

Since allantoin is an active ingredient in skin creams production for skin wounds treatment and skin cares, its solubility was studied in supercritical CO2 as a clean and efficient technique. Two significant independent variables involving pressure (200–500 bar) and temperature (30–60 °C) effect on the allantoin solubility with using ethanol (as a polar cosolvent) were studied. Therefore, Central Composite Design (CCD) under Response Surface Methodology (RSM) was applied to design the experiments. 13 experiments with their operating conditions were proposed by the software. According to the experiments, the optimum solubility of allantoin (0.003745) was found at pressure of 202 bar and temperature of 51 °C while the maximum solubility (0.003677) was experimentally obtained at pressure of 200 bar and temperature of 60 °C in supercritical CO2. Furthermore, Mohsen-Nia-Modarress-Mansoori (MMM) equation of state, which is a proper equation for the biological molecules’ solubility prediction, was applied to find the allantoin solubility, thermodynamically. According to the calculated errors, a good agreement between experimental and modeled data was found. Finally, the binary coefficients were reported.

Solubility of Methane in Ionic Liquids for Gas Removal Processes Using a Single Multilayer Perceptron Model

In this work, four hundred and forty experimental solubility data points of 14 systems composed of methane and ionic liquids are considered to train a multilayer perceptron model. The main objective is to propose a simple procedure for the prediction of methane solubility in ionic liquids. Eight machine learning algorithms are tested to determine the appropriate model, and architectures composed of one input layer, two hidden layers, and one output layer are analyzed. The input variables of an artificial neural network are the experimental temperature (T) and pressure (P), the critical properties of temperature (Tc) and pressure (Pc), and the acentric (ω) and compressibility (Zc) factors. The findings show that a (4,4,4,1) architecture with the combination of T-P-Tc-Pc variables results in a simple 45-parameter model with an absolute prediction deviation of less than 12%.

Prediction of ionic liquid solubilities in supercritical CO2 + co-solvent systems using Peng–Robinson equation of state with accurate critical temperature

Supercritical fluid deposition (SCFD) using supercritical carbon dioxide (CO2) to impregnate substrates with ionic liquids (ILs) has attracted considerable attention for the preparation of porous-supported ILs. Because ILs are generally insoluble in pure CO2, it is essential to estimate the solubilities of ILs in supercritical CO2 + co-solvent systems for developing the SCFD process. In this study, a methodology was developed to predict the solubility of ILs in supercritical CO2 with co-solvents using the corresponding binary phase-equilibrium data. For the prediction, the binary interaction parameters for the Peng–Robinson equation of state (PR-EoS) were determined using the correlations to each phase-equilibrium system (i.e., IL + CO2, CO2 + co-solvent, and IL + co-solvent). The predictive model using the PR-EoS, which included a re-determined critical temperature as a pure component parameter of ILs, could reproduce the effects of the temperature, pressure, and co-solvent concentration on the IL solubilities in the supercritical fluid phase without any fitting parameters for the ternary systems. The average absolute relative deviations based on the logarithmic scale for the predictions were <23 %, indicating that the methodology was acceptable for describing the IL solubilities in the supercritical CO2 + co-solvent system.

Screening TLR4 Binding Peptide from Naja atra Venom Glands Based on Phage Display.

Toll-like receptor 4 (TLR4) is a crucial inflammatory signaling pathway that can serve as a potential treatment target for various disorders. A number of inhibitors have been developed for the TLR4 pathway, and although no inhibitors have been approved for clinical use, most have been screened against the TLR4-MD2 conformation. The venom gland is the organ of venomous snakes that secretes substances that are toxic to other animals. The level of gene transcription in venom glands is different from that in other tissues, includes a large number of biologically active ingredients, and is an important natural resource for the development of new drugs. We constructed a T7 phage display library using the cobra (Naja atra) venom gland from the Guangdong Snake Breeding Plant and performed three rounds of screening with TLR4 as the target, randomly selecting monoclonal phage spots for PCR followed by Sanger sequencing. The obtained sequences were subjected to length analysis, molecular docking, solubility prediction, and stability prediction, and a peptide containing 39 amino acids (NA39) was finally screened out. The BLAST results indicated that NA39 was a sequence in RPL19 (Ribosomal Protein L19). After peptide synthesis, the binding ability of NA39 to TLR4 was verified by the surface plasmon resonance (SPR) technique. In this study, a new peptide that can specifically bind TLR4 was successfully screened from the cobra venom gland cDNA library, further demonstrating the effectiveness of phage display technology in the field of drug discovery.