Molecular Descriptors Research Articles

Theoretical Exploration of the Antioxidant Activity, Chemopreventive, and Antineoplastic Potentials of Molecules Present in Morinda lucida, Momordica charantia, and Vernonanthura polyanthes

Cancer is one of the leading causes of death worldwide and one of the top four causes of premature death in most countries. Several studies have shown that secondary metabolites found in medicinal plants have anticancer, antimutagenic, anti-genotoxic, and chemopreventive effects. Ethnobotany knowledge points to Morinda lucida, Momordica charantia, and Vernonanthura polyanthes as anticancer plants, turning them into promising candidates for cancer treatment. This study analyzed M. lucida, M. charantia, and V. polyanthes compounds to identify potential antioxidant, chemopreventive, and anticancer agents through in silico analysis. We performed pharmacokinetics, biological activities, and toxicological predictions besides molecular docking. Theoretical calculations on thermodynamic grounds of the antioxidant properties were determined using density functional theory. The molecules damnacanthal, momordicin I, and quercetin-3-[Formula: see text]-rutinoside identified in M. lucida, M. charantia, and V. polyanthes, respectively, presented good anticancer potential and were chosen for the molecular docking study and DFT calculations. According to Lipinski’s criteria, damnacanthal, momordicin I, and quercetin-3-[Formula: see text]-rutinoside were classified as potential drugs, especially for the pharmacokinetics criteria. Damnacanthal and momordicin I had high absorption in the gastrointestinal treatment, while quercetin-3-[Formula: see text]-rutinoside had low absorption. These compounds also demonstrated antineoplastic, antimutagenic, and cancer-preventive activity, according to the in silico analysis. In this sense, damnacanthal, momordicin I, and quercetin-3-[Formula: see text]-rutinoside were chosen to test their potential interactions with the beta estrogen receptor, vascular endothelial growth factor receptor 2, and xanthine dehydrogenase, respectively, and intermolecular interactions were determined. Regarding the antioxidant potential of the structures, two different mechanisms were scrutinized with the help of their respective molecular descriptors, such as hydrogen atom transfer (HAT) and one-electron transfer (ET). In conclusion, this computational study revealed that damnacanthal, momordicin I, and quercetin-3-[Formula: see text]rutinoside are promising chemoprotective and anticancer molecules with favorable pharmacokinetics and toxicological properties.

Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen.

Deep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental solubility data were collected for all DES systems. A machine learning model was developed using COSMO-RS molecular descriptors to predict solubility. All studied DESs exhibited a cosolvency effect, increasing drug solubility at modest concentrations of water. The model accurately predicted solubility for ibuprofen, ketoprofen, and related analogs (flurbiprofen, felbinac, phenylacetic acid, diphenylacetic acid). A machine learning approach utilizing COSMO-RS descriptors enables the rational design and solubility prediction of DES formulations for improved pharmaceutical applications.

Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach.

Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES's suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation

In the context of carbon neutrality and carbon peaking, molecular management has become a focus of the petrochemical industry. The key to achieving molecular management is molecular reconstruction, which relies on rapid and accurate calculation of oil properties. Focusing on naphtha, we proposed a novel property prediction model construction procedure (MDs-NP) employing molecular dynamics simulations for property collections and gamma distribution from real analytical data for calculating mole fractions of simulation mixtures. We calculated 348 sets of mixture properties data in the range of 273 K–300 K by molecular dynamics simulations. Molecular feature extraction was based on molecular descriptors. In addition to descriptors based on open-source toolkits (RDKit and Mordred), we designed 12 naphtha knowledge (NK) descriptors with a focus on naphtha. Three machine learning algorithms (support vector regression, extreme gradient boosting and artificial neural network) were applied and compared to establish models for the prediction of the density and viscosity of naphtha. Mordred and NK descriptors + support vector regression algorithm achieved the best performance for density. The selected RDKFp and NK descriptors + artificial neural network algorithm achieved the best performance for viscosity. Using ablation studies, T, P_w and CC(C)C are three effective descriptors in NK that can improve the performance of the property prediction models. MDs-NP has the potential to be extended to more properties as well as more-complex petroleum systems. The models from MDs-NP can be used for rapid molecular reconstruction to facilitate construction of data-driven models and intelligent transformation of petrochemical processes.

Influence of molecular descriptors of plant volatilomics on fumigant action against the three major stored product beetle pests.

Plant volatilomics such as essential oils (EOs) and volatile phytochemicals (PCs) are known as potential natural sources for the development of biofumigants as an alternative to conventional fumigant pesticides. This present work was aimed to evaluate the fumigant toxic effect of five selected EOs (cinnamon, garlic, lemon, orange, andpeppermint) and PCs (citronellol, limonene, linalool, piperitone, andterpineol) against the Callosobruchus maculatus, Sitophilus oryzae, and Tribolium castaneum adults. Furthermore, for the estimation of the relationship between molecular descriptors and fumigant toxicity of plant volatiles, quantitative structural activity relationship (QSAR) models were developed using principal component analysis and multiple linear regression. Amongst the tested EOs, garlic EO was found to be the most toxic fumigant. The PCs toxicity analysis revealed that terpineol, limonene, linalool, and piperitone as potential fumigants to C. maculatus (< 20 µL/L air of LC50), limonene and piperitone as potential fumigants to T. castaneum (14.35 and 154.11 µL/L air of LC50, respectively), and linalool and piperitone as potential fumigants to S. oryzae (192.27 and 69.10 µL/L air of LC50, respectively). QSAR analysis demonstrated the role of various molecular descriptors of EOs and PCs on the fumigant toxicity in insect pest species. In specific, dipole and Randic index influence the toxicity in C. maculatus, molecular weight and maximal projection area influence the toxicity in S. oryzae, and boiling point and Dreiding energy influence the toxicity in T. castaneum. The present findings may provide insight of a new strategy to select effective EOs and/or PCs against stored product insect pests.

Amino acids as stabilizers for lysozyme during the spray-drying process and storage

Amino acids (AAs) have been used as excipients in protein formulations both in solid and liquid state products due to their stabilizing effect. However, the mechanisms by which they can stabilize a protein have not been fully elucidated yet. The purpose of this study was to investigate the effect of AAs with distinct physicochemical properties on the stability of a model protein (lysozyme, LZM) during the spray-drying process and subsequent storage. Molecular descriptor based multivariate data analysis was used to select distinct AAs from the group of 20 natural AAs. Then, LZM and the five selected AAs (1:1 wt ratio) were spray-dried (SD). The solid form, residual moisture content (RMC), hygroscopicity, morphology, secondary/tertiary structure and enzymatic activity of LZM were evaluated before and after storage under 40 °C/75 % RH for 30 days. Arginine (Arg), leucine (Leu), glycine (Gly), tryptophan (Trp), aspartic acid (Asp) were selected because of their distinct properties by using principal component analysis (PCA). The SD LZM powders containing Arg, Trp, or Asp were amorphous, while SD LZM powders containing Leu or Gly were crystalline. Recrystallization of Arg, Trp, Asp and polymorph transition of Gly were observed after the storage under accelerated conditions. The morphologies of the SD particles vary upon the different AAs formulated with LZM, implying different drying kinetics of the five model systems. A tertiary structural change of LZM was observed in the SD powder containing Arg, while a decrease in the enzymatic activity of LZM was observed in the powders containing Arg or Asp after the storage. This can be attributed to the extremely basic and acidic conditions that Arg and Asp create, respectively. This study suggests that when AAs are used as stabilizers instead of traditional disaccharides, not only do classic vitrification theory and water replacement theory play a role, but the microenvironmental pH conditions created by basic or acidic AAs in the starting solution or during the storage of solid matter are also crucial for the stability of SD protein products.

Precise prediction of CO2 separation performance of metal–organic framework mixed matrix membranes based on feature selection and machine learning

Nowadays, the price of fossil fuels keeps setting new records, escalating continuing concerns about global warming from CO2 production from fuel combustion. As a promising membrane separation technique dealing with carbon capture, metal–organic framework (MOF) mixed matrix membranes (MMMs) have been extensively studied. Herein, a genetic algorithm (GA) optimized artificial neural network (ANN) was developed to form prediction model of MOF MMMs performances towards CO2/N2 separation. The MOF properties, polymer properties, and the operating conditions were used as the characteristic variables. To overcome the limitation, molecular descriptors were incorporated to reflect the physicochemical properties of polymers and target encoding was applied to digitalize the MOF and polymer types. In addition, recursive feature elimination algorithm was used to filter the optimal feature subset and Shapley additive explanations was utilized to analyze the feature importance. The results demonstrated that the model has a dramatically improved prediction performance than other machine learning methods.

Multi-strategy enhanced snake optimizer for quantitative structure-activity relationship modeling

Quantitative structure-activity relationship modeling techniques are widely employed in diverse areas of chemistry, including environmental risk assessment of compounds and virtual screening of drugs. The modeling process entails optimizing the hyper parameters of the model and selecting appropriate molecular descriptors. It is a typical optimization problem with high-dimensional, nonlinear, continuous and discrete variables coexisting. To address this challenge, this paper proposes an enhanced snake optimizer with multiple strategies. First, new food emergence and temperature change mechanisms are introduced to balance the exploration and exploitation. Second, a death and regeneration mechanism is employed to enhance the algorithm's ability to escape from local optima. Third, new formulas for food searching, fighting, mating and eating are designed to simplify the complexity of the algorithm and improve its search efficiency and accuracy. Fourth, the performance of the proposed algorithm is evaluated using the CEC2017 benchmark functions, eight engineering cases with continuous variables, and six feature selection cases with discrete variables. Experimental results demonstrate that the algorithm significantly outperforms comparison algorithms. Finally, the algorithm was applied to quantitative structure-activity relationship modeling for environmental toxicity prediction. Compared with other algorithms, the algorithm can better configure hyper-parameters and select fewer and more effective features for environmental toxicity prediction model. It will be an effective tool to improve the performance of quantitative structure-activity relationship models.

The Euler Sombor index of a graph

AbstractA novel geometric method is proposed for constructing vertex‐degree‐based molecular structure descriptors, which is the perimeter of an ellipse whose focal points represent the degree‐point and its dual‐point of a pair of adjacent vertices in a graph . We first analyze the chemical applicability of this elliptic Sombor index, and then establish its mathematical properties and determine the extremal values for this elliptic Sombor index among all (molecular) trees and characterize the corresponding extremal graphs.

Modelling of chromatographic and electrophoretic behaviour of imidazoline and alpha adrenergic receptors ligands under different acid-base conditions

Background and Purpose: The ligands of the imidazoline and α-adrenergic receptors are mainly imidazoline and guanidine derivatives, known as centrally-acting antihypertensives and compounds with potential use in various neurological disorders. The extent of their ionisation has a major influence on their behaviour in the different analytical systems. The main objective of this work was to compare the mechanism of chromatographic retention and electrophoretic mobility under acidic, neutral and basic conditions. Experimental Approach: Multiple Linear Regression and Partial Least Squares Regression were applied for the QSRR (quantitative structure-retention relationship) and QSMR (quantitative structure-mobility relationship) modelling and to select the most important molecular parameters describing the chromatographic and electrophoretic behaviour of the investigated compounds. Key Results: The most important molecular descriptors, such as the chemical composition of the compounds, lipophilicity, polarizability and molecular branching, in the selected QSRR models showed that an important insight into the retention behaviour can be derived from the 0D-, 1D- and 2D-descriptors. The electrophoretic mobility could be explained by 2D- and 3D-descriptors, which provide information on the molecular mass, size and complexity, as well as on the influence of charge transfer and electronic properties on the migration behaviour. Conclusion: All created QSRR/QSMR models met the stringent validation criteria and showed high potential in describing the chromatographic and electrophoretic behaviour of investigated compounds.

Ensemble multiclassification model for predicting developmental toxicity in zebrafish

In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.

Structure-property modeling of coumarins and coumarin-related compounds in pharmacotherapy of cancer by employing graphical topological indices.

Coumarins, a subgroup of colorless and crystalline oxygenated heterocyclic compounds originally discovered in the plant Dipteryx odorata, were the subject of a recent study investigating their quantitative structure-activity relationship (QSAR) in cancer pharmacotherapy. This study utilized graph theoretical molecular descriptors, also known as topological indices, as a numerical representation method for the chemical structures embedded in molecular graphs. These descriptors, derived from molecular graphs, play a pivotal role in quantitative structure-property relationship (QSPR) analysis. In this paper, intercorrelation between the Balban index, connective eccentric index, eccentricity connectivity index, harmonic index, hyper Zagreb index, first path Zagreb index, second path Zagreb index, Randic index, sum connectivity index, graph energy and Laplacian energy is studied on the set of molecular graphs of coumarins. It is found that the pairs of degree-based indices are highly intercorrelated. The use of these molecular descriptors in structure-boiling point modeling was analyzed. Finally, the curve-linear regression between considered molecular descriptors with physicochemical properties of coumarins and coumarin-related compounds is obtained.

Heterocyclic-Based Analogues against Sarcine-Ricin Loop RNA from Escherichia coli: In Silico Molecular Docking Study and Machine Learning Classifiers.

Heterocyclic-based drugs have strong bioactivities, are active pharmacophores, and are used to design several antibacterial drugs. Due to the diverse biodynamic properties of well-known heterocyclic cores, such as quinoline, indole, and its derivatives, they have a special place in the chemistry of nitrogen-containing heterocyclic molecules. The objective of this study is to analyze the interaction of several heterocyclic molecules using molecular docking and machine learning approaches to find out the possible antibacterial drugs. The molecular docking analysis of heterocyclic-based analogues against the sarcin-Ricin Loop RNA from E. coli with a C2667-2'-OCF3 modification (PDB ID: 6ZYB) is discussed. Many heterocyclic-based derivatives show several residual interaction, affinity, and hydrogen bonding with sarcin-Ricin Loop RNA from E. coli with a C2667-2'-OCF3 alteration which are identified by the investigation of in silico molecular docking analysis of such heterocyclic derivatives. The dataset from the molecular docking study was used for additional optimum analysis, and the molecular descriptors were classified using a variety of machine learning classifiers, including the GB Classifier, CB Classifier, RF Classifier, SV Classifier, KNN Classifier, and Voting Classifier. The research presented here showed that heterocyclic derivatives may operate as potent antibacterial agents when combined with other compounds to produce highly efficient antibacterial agents.

Prediction of DNA-binding Sites in Transcriptions Factor in Fur-like Proteins Using Machine Learning and Molecular Descriptors

Introduction: Transcription factors are of great interest in biotechnology due to their key role in the regulation of gene expression. One of the most important transcription factors in gramnegative bacteria is Fur, a global regulator studied as a therapeutic target for the design of antibacterial agents. Its DNA-binding domain, which contains a helix-turn-helix motif, is one of its most relevant features. Methods: In this study, we evaluated several machine learning algorithms for the prediction of DNA-binding sites based on proteins from the Fur superfamily and other helix-turn-helix transcription factors, including Support-Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Naive Bayes (NB). We also tested the efficacy of using several molecular descriptors derived from the amino acid sequence and the structure of the protein fragments that bind the DNA. A feature selection procedure was employed to select fewer descriptors in each case by maintaining a good classification performance. Results: The best results were obtained with the SVM model using twelve sequence-derived attributes and the DT model using nine structure-derived features, achieving 82% and 76% accuracy, respectively. Conclusion: The performance obtained indicates that the descriptors we used are relevant for predicting DNA-binding sites since they can discriminate between binding and non-binding regions of a protein.

Polymer design using machine learning: A quest for high glass transition temperature

This research introduces an advanced data-centric framework tailored for polymer design. Developing a machine learning (ML) model utilizing molecular descriptors, the study aims to forecast the glass transition temperature of polymers. Employing the Retrosynthetically Interesting Chemical Substructures (BRICS) technique, a set of 10,000 novel polymers is synthesized, followed by predicting their glass transition temperature (Tg) values using a pre-existing ML model trained for this purpose. Gradient Boosting and Extra Trees models outperform K Neighbors and Random Forest (KNN) models in predicting performance parameters with R2 scores of 0.99 and 0.99, and lower root mean squared error values of 24 and 32 K, respectively. The selection of polymers is done on the basis of glass transition temperature, those with higher glass transition temperature are selected. We evaluate the ease of synthetic accessibilities of the selected polymers alongwith their structural variations which indicates a diverse range among them. Through the successful identification and enhancement of novel polymers, these innovative methods greatly enhance the possibilities of discovering superior materials tailored for cutting-edge applications.

Computational analysis of antiviral drugs using topological descriptors

Many health challenges are attributed to viral infections, which represent significant concerns in public health. Among these infections, diseases such as herpes simplex virus (HSV), cytomegalovirus (CMV), and varicella-zoster virus (VZV) infections have garnered attention due to their prevalence and impact on human health. There are specific antiviral medications available for the treatment of these viral infections. Drugs like Cidofovir, Valacyclovir, and Acyclovir are commonly prescribed. These antiviral drugs are known for their efficacy against herpesviruses and related viral infections, leveraging their ability to inhibit viral DNA polymerase. A molecular descriptor is a numerical value that correlates with specific physicochemical properties of a molecular graph. This article explores the calculation of distance-based topological descriptors, including the Trinajstic, Mostar, Szeged, and PI descriptors for the aforementioned antiviral drugs. These descriptors provide insights into these drugs’ structural and physicochemical characteristics, aiding in understanding their mechanism of action and the development of new therapeutic agents.

Theoretical Study of Radical Scavenging Antioxidant Activity of Coumarin Derivatives in Licorice Using DFT

Nowdays, much research is focused on exploring antioxidant activity based on natural compounds. Licorice, a traditional natural product rich in antioxidant components, has been widely used in clinical medicine. However, there is a lack of theoretical studies on the active components in licorice. In this context, the antioxidant activity of several common active components of coumarin derivatives in licorice was investigated using a density flooding theory (DFT) approach. The free radical scavenging ability of the compounds was achieved by hydrogen atom transfer (HAT), sequential electron transfer proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET) mechanisms. The effect of solvent on antioxidant activity was also investigated. It was found that in the gas and benzene phases, the compounds studied preferred to undergo the HAT mechanism, while SPLET was favoured in polar solvents. In addition to the above mechanism, the HOMO and LUMO energies of the compounds as well as molecular descriptors and electrostatic potential maps were calculated and applied to predict the antioxidant activity of the compounds. The above studies allow a better understanding of the mechanism of free radical scavenging activity of coumarin derivatives from licorice.

DFT Calculations, Molecular Docking, and Pharmacological Properties Investigation for 5-Benzoxazolecarboxylic Acid as a Target Anti-Cancer Agent

In this study, the electronic properties of the 5-Benzoxazolecarboxylic acid molecule, a benzoxazole derivative, were calculated at the DFT/B3LYP/6-311++G(d,p) level of theory. Electronic properties and chemical reactivity of the optimized structure, such as Frontier molecular orbital (FMO), global and chemical reactivity descriptors, molecular electrostatic potential (MEP), and charge analyses (APT, Hirshfeld, and NBO), were investigated. Also, electronic properties are supported by electron localization function (ELF) and localized orbital locator (LOL) analyses. Toxicity effects such as mutagenic, tumorigenic, irritant, reproductive effect, and physicochemical properties such as druglikeness and drugscore were investigated. Molecular docking studies were conducted with the vascular endothelial growth factor receptor VEGFR-2 and the PARP-2 inhibitor, which is involved in many critical cellular processes, including DNA single-stranded fracture repair and cell death control, and its effectiveness in cancer treatment was investigated.

Predictive computational models for assessing the impact of co-milling on drug dissolution

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

Rational screening of ionic liquids as phase transfer catalysts for aromatic O- and S-glycosidations examplified by acetobromo-alpha-D-galactose with phenolic compound

The heterogeneous liquid–liquid O-glycosidation is a crucial step in pharmaceutical synthesis. Herein, a theoretical methodology based on COSMO-RS, quantum chemical calculations combined with Transformer-CNN model was used to screen ionic liquids (ILs) as phase transfer catalysts (PTCs) to intensify O-glycosidation between 4-nitrophenol and 2,3,4,6-tetra-O-acetyl-α-D-galactopyranosyl bromide. The anions of ILs were screened by the evaluation of ILs hydrophilicity via infinite dilution selectivity of ILs and the molecular descriptor HB_acc3 of the anion using COSMO-RS, a predictive model for thermodynamic properties. Additionally, the binding energy between the phenoxide anion and IL cation was used to assess reaction extent in the aqueous phase through quantum chemical calculation. The cations of ILs were evaluated by the infinite dilution selectivity of the combination between phenoxide anion and IL cations, and physical properties including viscosity and melting point were also predicted using an advanced Transformer-CNN model. The screened IL 1-octyl-3-methylimidazolium acetate and 1-hexyl-3-methylimidazolium acetate were demonstrated its superior catalytic activity for the aromatic O-glycosidation of not only 4-nitrophenol, but also various phenols and thiophenols. Moreover, the presence of PTCs enhances the likelihood of the reactant migrating into another phase. This behavior is validated by COSMO-RS calculated liquid–liquid equilibrium (LLE) and interaction energy analysis. The method provides great potential for screening and designing ILs as phase transfer catalysts for heterogeneous liquid–liquid reactions.