Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR

Abstract

Quantitative structure-activity relationship (QSAR) modelling of 55 focal adhesion kinase (FAK) (EC 2.7.10.2) inhibitors of triazinic nature was performed using the Monte Carlo method. The QSAR models were designed by CORAL software, and optimal descriptors were calculated with the simplified molecular input line entry system (SMILES). Four splits were made from the triazinic derivative data by random division into training, invisible training, calibration and validation sets. The QSAR results from these four random splits were robust, very simple, predictive and reliable. The best statistical parameters of the validation set (r² = 0.8398 and Q² = 0.7722) for the QSAR equation for split 3 with IIC = 0.9127 were obtained. The predictive potential of QSAR models of FAK inhibitors was explored by applying the index of ideality of correlation (IIC), which is a new criterion for the prediction of the potential for quantitative structure-property activity relationships (QSPRs/QSARs). The present method follows OECD principles.