Solubility of ʟ-arginine in twelve mono-solvents: Solvent effects, molecular simulations and model correlations DOI Creative Commons

Yusheng Xiao,

Shen Hu,

Long Zhao

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 5, 2024

Abstract The solubility data of ʟ-arginine were measured by the static gravimetric method in twelve mono-solvents (water, methanol, ethyl acetate, ethanol, n-hexane, 2-butanone, isopropanol, 2-butanol, dichloromethane (DCM), dimethylformamide (DMF), 1,4-dioxane and acetonitrile) at T = 283.15-323.15 K P 101.2kPa. In solvents used, increases with increasing temperature. order 298.15 12 was ranked as: water > methanol acetate ethanol n-hexane 2-butanone isopropanol 2-butanol DCM DMF acetonitrile. According to results, among factors affecting dissolution behavior, Dimroth Reichardt’s polarity parameters (ET(30)) plays a dominant role, is also affected hydrogen bond cohesive energy density. Molecular modeling including Hirshfeld surface (HS) analysis molecular electrostatic potential (MEPS) employed understand internal interactions within crystals. results interaction region indicator (IRI) show that selected alcohol decreases growth solvent carbon chain modified Apelblat model, Yaws model Margules can be well used correlate data. Moreover, Akaike information criterion evaluate fitting accuracy three models. evaluation more suitable for this work. This study enriches provides basic production application ʟ-arginine.

Язык: Английский

Artificial Intelligence aided pharmaceutical engineering: Development of hybrid machine learning models for prediction of nanomedicine solubility in supercritical solvent DOI
Chunchao Chen

Journal of Molecular Liquids, Год журнала: 2024, Номер 397, С. 124127 - 124127

Опубликована: Янв. 26, 2024

Язык: Английский

Процитировано

8
Machine learning aided pharmaceutical engineering: Model development and validation for estimation of drug solubility in green solvent DOI

Di Meng,

Zhenyu Liu

Journal of Molecular Liquids, Год журнала: 2023, Номер 392, С. 123286 - 123286

Опубликована: Окт. 12, 2023

Язык: Английский

Процитировано

15
Solubility of N-Acetyl-L-glutamine in twelve Mono-solvents: Characterization, Determination, Analysis, and model correlation DOI
Dandan Liu, Yongjie Wang, Shujing Zhang

и другие.

Journal of Molecular Liquids, Год журнала: 2024, Номер 405, С. 125024 - 125024

Опубликована: Май 19, 2024

Язык: Английский

Процитировано

5
Investigation on solid–liquid equilibrium behavior of 4-cyanobenzoic acid in fourteen mono-solvents: Determination, correlation, molecular simulation and thermodynamic analysis DOI
Yameng Wan, Yanxun Li, Keyu Chen

и другие.

Journal of Molecular Liquids, Год журнала: 2023, Номер 391, С. 123184 - 123184

Опубликована: Окт. 1, 2023

Язык: Английский

Процитировано

12
Experimental analysis and prediction of radionuclide solubility using machine learning models: Effects of organic complexing agents DOI
Bolam Kim, Amaranadha Reddy Manchuri, Gi-Taek Oh

и другие.

Journal of Hazardous Materials, Год журнала: 2024, Номер 469, С. 134012 - 134012

Опубликована: Март 12, 2024

Язык: Английский

Процитировано

4
Insight into the Dissolution Behavior of Benzimidazole in 19 Monosolvents: Solubility, Characterization, Determination, Analysis, and Model Correlation DOI

Min Ding,

Long Zhao, Xing Xin

и другие.

Journal of Chemical & Engineering Data, Год журнала: 2025, Номер unknown

Опубликована: Фев. 6, 2025

Benzimidazole has many biological activities and is widely used in the pharmaceutical chemical industries. Under conditions of P = 101.2 kPa T 273.15–323.15 K, solubility benzimidazole 19 monosolvents was determined using static gravimetric method. The positively correlated with temperature varied among solvents, n-pentanol having highest value. Four models (modified Apelblat, NRTL, UNIQUAC, Margules) were to correlate data, modified Apelblat model best fitting effect. Then, thermodynamic properties mixing process estimated by NRTL model. In addition, internal interactions analyzed molecular electrostatic potentials (MEPs) Hirshfeld surface (HS), revealing a strong solvent-benzimidazole hydrogen-bonding tendency. Furthermore, Hansen parameters (HSPs), interaction region indicator (IRI), energy evaluate benzimidazole's monosolvents. Results show that main factors influencing behavior include solvent polarity (ET(30)) HSPs. These experimental results can be for purification, crystallization, industrial applications as well similar substances.

Язык: Английский

Процитировано

0
Hybrid Semi-mechanistic and Machine Learning Solubility Regression Modeling for Crystallization Process Development DOI
Gustavo Lunardon Quilló, Satyajeet Bhonsale, A. Collas

и другие.

Crystal Growth & Design, Год журнала: 2025, Номер unknown

Опубликована: Фев. 10, 2025

Solubility regression modeling is foundational for several chemical engineering applications, particularly crystallization process development. Traditionally, these models rely on parametric semimechanistic approaches such as the Van't Hoff Jouyban-Acree (VH-JA) cosolvency model. Although generally provide narrow prediction intervals, they can exhibit increased bias when dealing with significant solute heat capacities or complex mixture effects. This study explores machine learning, including Random Forests, Support Vector Machines, Gaussian Process Regression, and Neural Networks, potential alternatives. While most learning offered a lower training error, it was observed that their predictive quality quickly deteriorates further from data. Hence, hybrid approach explored to leverage low of variance VH-JA model through heterogeneous locally weighted bagging ensembles. Key methodology quantifying, tracking, minimizing uncertainty using ensemble. illustrated case solubility ketoconazole in binary mixtures 2-propanol water. The optimal ensemble, comprising 58% stepwise 42% models, reduced root-mean-squared error maximum absolute percentage by ≈30% compared full VH-JA, while preserving comparable interval.

Язык: Английский

Процитировано

0
Development of SVM-based machine learning model for estimating lornoxicam solubility in supercritical solvent DOI Creative Commons
Mingji Zhang, Wael A. Mahdi

Case Studies in Thermal Engineering, Год журнала: 2023, Номер 49, С. 103268 - 103268

Опубликована: Июль 7, 2023

This paper investigates the application of Support Vector Regression with Quadratic Kernel (QSVR) for modeling solubility lornoxicam in supercritical carbon dioxide. The dataset comprises temperature (T) and pressure (P) as input variables, while (Y) serves output variable entire modeling. is measured Kelvin (K), bar inputs models. To improve predictive performance QSVR model, three distinct hyper-parameter optimization techniques, namely Genetic Algorithm (GA), Tabu Search (TS), Bayesian Hyperparameter Optimization (BHO) are employed. These methods utilized to fine-tune hyper-parameters model enhance its accuracy. BHO-QSVR achieved an impressive R2 score 0.96725, indicating a strong fit between predicted actual values. Additionally, it exhibited Mean Absolute Error (MAE) 1.75666E-05 maximum error 3.02849E-05. Comparatively, GA-QSVR TS-QSVR models also performed well, achieving scores 0.95346 0.95882, respectively. MAE 1.56725E-05 4.92382E-05, 1.84075E-05 5.02443E-05.

Язык: Английский

Процитировано

8
Solubility of 5-chloro-2-nitroaniline in twelve mono-solvents: Characterization, determination, analysis, and model correlation DOI

Min Ding,

Long Zhao,

Xing Xin

и другие.

Journal of Molecular Liquids, Год журнала: 2024, Номер 408, С. 125240 - 125240

Опубликована: Июнь 26, 2024

Язык: Английский

Процитировано

2
Data-driven models and comparison for correlation of pharmaceutical solubility in supercritical solvent based on pressure and temperature as inputs DOI Creative Commons
Mohammed F. Aldawsari, Wael A. Mahdi, Jawaher Abdullah Alamoudi

и другие.

Case Studies in Thermal Engineering, Год журнала: 2023, Номер 49, С. 103236 - 103236

Опубликована: Июнь 29, 2023

Data-driven models were employed for the solubility correlation, while focus was on modeling of raloxifene drug and density carbon dioxide based temperature (T) pressure (P) inputs. Three Machine Learning models, namely Multilayer Perceptron (MLP), Bayesian Ridge Regression (BRR), LASSO regression, optimized using MPHPT method hyper-parameter tuning. The dataset consisted experimental measurements (y), CO2 density. For prediction density, MLP model exhibited excellent performance with an R2 score 0.99726, demonstrating a significant level association between anticipated observed values. mean squared error (MSE) 9.8721E+01, absolute percentage (MAPE) 1.78565E-02, maximum 1.86395E+01. BRR achieved slightly lower accuracy, scores 0.83317 0.83001, respectively. Regarding drug, demonstrated strong predictive capability 0.99343. MSE 3.0869E-02, MAPE 4.02666E-02, 3.01133E-01. also provided reasonable predictions, 0.90955 0.8891, However, they higher MSEs MAPEs compared to model.

Язык: Английский

Процитировано

5