Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals

Chemoinformatic modelling of the antioxidant activity of phenolic compounds

BACKGROUND

Antioxidants are chemicals used to protect foods from deterioration by neutralizing free radicals and inhibiting the oxidative process. One approach to investigate the antioxidant activity is to develop quantitative structure-activity relationships (QSARs).

RESULTS

A curated database of 165 structurally heterogeneous phenolic compounds with the Trolox equivalent antioxidant capacity (TEAC) was developed. Molecular geometries were optimized by means of the GFN2-xTB semiempirical method and diverse molecular descriptors were obtained afterwards. For model development, V-WSP unsupervised variable reduction was used before performing the genetic algorithms-variable subset selection (GAs-VSS) to construct the best five-descriptor multiple linear regression model. The coefficient of determination and the root mean square error were used to measure the performance in calibration (R²  = 0.789 and RMSEC = 0.381), and test set prediction (Q²  = 0.748 and RMSEP = 0.416), along several cross-validation criteria. To thoroughly understand the TEAC prediction, a fully explained mechanism of action of the descriptors is provided. In addition, the applicability domain of the model defined a theoretical chemical space for reliable predictions of new phenolic compounds.

CONCLUSION

This in silico model conforms to the five principles stated by the Organisation for Economic Co-operation and Development. The model might be useful for virtual screening of the antioxidant chemical space and for identifying the most potent molecules related to an experimental measurement of TEAC activity. In addition, the model could assist chemists working on computer-aided drug design for the synthesis of new targets with improved activity and potential uses in food science. © 2023 Society of Chemical Industry.

Proanthocyanidin Structure-Activity Relationship Analysis by Path Analysis Model

To fully explore the influence mechanism of interactions between different monomer units of proanthocyanidins (PAs) on biological activity, a path analysis model of the PA structure-activity relationship was proposed. This model subdivides the total correlation between each monomer unit and activity into direct and indirect effects by taking into account not only each monomer unit but also the correlation with its related monomer units. In addition, this method can determine the action mode of each monomer unit affecting the activity by comparing the direct and total indirect effects. Finally, the advantage of this model is demonstrated through an influence mechanism analysis of Rhodiola crenulata PA monomer units on antioxidant and anti-diabetes activities.

In silico study of natural antioxidants

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

CO2 uptake prediction of metal–organic frameworks using quasi-SMILES and Monte Carlo optimization

The first report of quasi-SMILES-based QSPR models for CO2 capture of MOFs based on experimental data.

CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors

Global QSAR modelling was performed to predict the pIC₅₀ values of 233 diverse heterocyclic compounds as BTK inhibitors with the Monte Carlo algorithm of CORAL software using the DCW hybrid descriptors extracted from SMILES notations of molecules. The dataset of 233 BTK inhibitors was randomly split into training, invisible training, calibration and validation sets. The index of ideality of correlation was also applied to build and judge the predictability of the QSAR models. Eight global QSAR models based on the hybrid optimal descriptor using two target functions, i.e. TF₁ (W_IIC = 0) and TF₂ (W_IIC = 0.2) have been constructed. The statistical parameters of QSAR models computed by TF₂ are more reliable and robust and were used to predict the pIC₅₀ values. The model constructed for split 4 via TF₂ is regarded as the best model and the numerical values of r²_Train, r²_Valid, Q²_Train and Q²_Valid are equal to 0.7981, 0.7429, 0.7898 and 0.6784, respectively. By internal and external validation techniques, the predictability and reliability of the designed models have been assessed. The structural attributes responsible for the increase and decrease of pIC₅₀ of BTK inhibitors were also identified.

The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors

Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point (T_m). Therefore, in the present manuscript, the melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs. The Monte Carlo algorithm of CORAL software is applied to build up a robust QSPR model to calculate the values T_m of 353 imidazolium ILs. Using a combination of SMILES and hydrogen-suppressed molecular graphs (HSGs), the hybrid optimal descriptor is computed and used to generate the QSPR models. Internal and external validation parameters are also employed to evaluate the predictability and reliability of the QSPR model. Four splits are prepared from the dataset and each split is randomly distributed into four sets i.e. training set (≈33%), invisible training set (≈31%), calibration set (≈16%) and validation set (≈20%). In QSPR modelling, the numerical values of various statistical features of the validation sets such as R_Validation², Q_Validation², and IIC_Validation are found to be in the range of 0.7846–0.8535, 0.7687–0.8423 and 0.7424–0.8982, respectively. For mechanistic interpretation, the structural attributes which are responsible for the increase/decrease of T_m are also extracted.

The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.

Can the Monte Carlo method predict the toxicity of binary mixtures?

Risk assessment of toxicants mainly is a result of experiments with single substances. However, toxicity in natural ecosystems typically does not result from single toxicant exposure but is rather a result of exposure to mixtures of toxicants. It is not surprising a mixture of toxicity is a subject of eco-toxicological interest for several decades. A quantitative structure-activity relationships (QSAR)-based approach is an attractive approach to assessing the joint effects in the binary mixtures. The validity of the proposed approach was demonstrated by comparing the predicted values against the experimentally determined values. Simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structures of components of two-component mixtures to build up QSAR. The SMILES-based models are improving if the Monte Carlo optimization aimed to define 2D-optimal descriptors apply the so-called index of ideality of correlation (IIC), which is a mathematical function of both the correlation coefficient and mean absolute error calculated for the positive and negative difference between observed and calculated values of toxicity. The average statistical quality of these models (for the validation set) is n=25, R²=0.95, and RMSE=0.375.

Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation

The present manuscript describes the synthesis, α-amylase inhibition, in silico studies and in-depth quantitative structure-activity relationship (QSAR) of a library of aroyl hydrazones based on benzothiazole skeleton. All the compounds of the developed library are characterized by various spectral techniques. α-Amylase inhibitory potential of all compounds has been explored, where compound 7n exhibits remarkable α-amylase inhibition of 87.5% at 50 µg/mL. Robust QSAR models are made by using the balance of correlation method in CORAL software. The chemical structures at different concentration with optimal descriptors are represented by SMILES. A data set of 66 SMILES of 22 hydrazones at three distinct concentrations are prepared. The significance of the index of ideality of correlation (IIC) with applicability domain (AD) is also studied at depth. A QSAR model with best Rvalidation2 = 0.8587 for split 1 is considered as a leading model. The outliers and promoters of increase and decrease of endpoint are also extracted. The binding modes of the most active compound, that is, 7n in the active site of Aspergillus oryzae α-amylase (PDB ID: 7TAA) are also explored by in silico molecular docking studies. Compound 7n displays high resemblance in binding mode and pose with the standard drug acarbose. Molecular dynamics simulations performed on protein-ligand complex for 100 ns, the protein gets stabilised after 20 ns and remained below 2 Å for the remaining simulation. Moreover, the deviation observed in RMSF during simulation for each amino acid residue with respect to Cα carbon atom is insignificant.

A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants

The Monte Carlo algorithm was applied to formulate a robust quantitative structure-property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES and hydrogen-suppressed molecular graph for both classes of compounds. Approximately 75% of the total experimental data set was randomly divided into training and invisible training sets, while approximately 25% was divided into calibration and validation sets. The authenticity and robustness of the developed QSPR models were also judged by the Index of Ideality of Correlation. In QSPR modelling of aliphatic compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.852-0.905, 0.815-0.894, 0.839-0.897 and 0.737-0.867, respectively. Whereas, in the QSPR modelling of phenyl-based compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.867-0.896, 0.852-0.865, 0.816-0.850 and 0.760-0.762, respectively. The structural attributes, which are promoters of l o g K e a q - increase/decrease are also extracted from the SMILES notation for mechanistic interpretation. These QSPR models can also be applied to compute the reaction rate constants of organic contaminants.

Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria

Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF₁) and index of ideality correlation target function (TF₂) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF₂ improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF₂ reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.

Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method

In this research, QSAR modeling was carried out through SMILES of compounds and on the basis of the Monte Carlo method to predict the antioxidant activity of 79 derivatives of pulvinic acid, 23 of coumarine, as well as nine structurally non-related compounds against three radiation sources of Fenton, gamma, and UV. QSAR model was designed through CORAL software, as well as a newer optimizing method well known as the index of ideality correlation. The full set of antioxidant compounds were randomly distributed into four sets, including training, invisible training, validation, and calibration; this division was repeated three times randomly. The optimal descriptors were picked up from a hybrid model by the combination of the hydrogen-suppressed graph and SMILES descriptors based on the objective function. These models' predictability was assessed on the sets of validation. The results of three randomized sets showed that simple, robust, reliable, and predictive models were achieved for training, invisible training, validation, and calibration sets of all three models. The central decrease/increase descriptors were identified. This simple QSAR can be useful to predict antioxidant activity of numerous antioxidants.