Application of GA-MLR method in QSPR modeling of stability constants of diverse 15-crown-5 complexes with sodium cation

Exploratory and machine learning analysis of the stability constants of HgII- triazene ligands complexes

Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation ( Q LOO 2  = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation.

SMILES-based QSAR and molecular docking study of xanthone derivatives as α-glucosidase inhibitors

Increasing diabetic population is one of the major health concerns all over the world. Inhibition of α-glucosidase is a clinically proved and attractive strategy to manage diabetes. In this study, robust and reliable QSAR models to predict α-glucosidase inhibitory potential of xanthone derivatives are developed by the Monte Carlo technique. The chemical structures are represented by SMILES notation without any 3D-optimization. The significance of the index of ideality correlation (IIC) with applicability domain (AD) is also studied in depth. The models developed using CORAL software by considering IIC criteria are found to be statistically more significant and robust than simple balance of correlation. The QSAR models are validated by both internal and external validation methods. The promoters of increase and decrease of activity are also extracted and interpreted in detail. The interpretation of developed models explains the role of different structural attributes in predicting the pIC₅₀ of xanthone derivatives as α-glucosidase inhibitors. Based on the results of model interpretation, modifications are done on some xanthone derivatives and 15 new molecules are designed. The α-glucosidase inhibitory activity of novel molecules is further supported by docking studies.

The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors

Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC₅₀) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r²) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC₅₀ values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC₅₀ values were predicted based on the average prediction of ten models.

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.