The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators

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

“…30,42 Here, to determine the robustness, reliability and the predictive capability of the QSPR models for T m of ILs three strategies were used: (i) internal validation or cross-validation; (ii) external validation and (iii) Y-scrambling or data randomization. 43,44 The mathematical relationship of different validation parameters employed herein is given in Table 1. Finally, IIC is employed to judge better models.…”

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

“…CORAL QSAR modeling provided the data related to structural attributes which were responsible for the enhancement and declination of the endpoint values [39,40]. If the values of correlation weights of a structural attribute were positive in all prepared model then it was said to be promoter of endpoint increase and opposite was true for promoter of endpoint decrease.…”

Monte Carlo based QSGFEAR: prediction of Gibb's free energy of activation at different temperatures using SMILES based descriptors

“…[3][4][5][6][7][8] The molecular structure of the compounds participating in the modelling process was represented by the Simplified Molecular Input Line Entry System (SMILES) 9 and a linear relationship between the endpoint and the SMILES based descriptor was calculated by the Monte Carlo method of optimization in the CORAL software. [10][11][12][13][14][15] Monte-Carlo methods are a class of computational algorithms that employ repeated random sampling to produce typically approximate solutions to a wide range of problems. 16,17 They are used to estimate unknown parameters when analytical or numerical solutions are unavailable or too challenging to utilize.…”