Multicellular target QSAR models for predicting of novel inhibitors against pancreatic cancer by Monte Carlo approach

Abstract

Pancreatic cancer is one of the most fatal malignant tumors, with a poor prognosis. Nowadays, computational methods for designing and predicting anticancer agents have gained increased attention. In this work, a QSAR study is conducted to further investigate the activity of 5797 agents against pancreatic cancer cell lines. QSAR models are a system of classification approaches that are used to construct and validate the predictive potential of created models. This approach demonstrates the prediction in the active (1) and inactive (−1) format. Here, the classification QSAR technique is employed to construct and evaluate models. The classification QSAR model has been constructed by the Monte Carlo method based on the representation of the molecular structure by SMILES (simplified molecular input line entry systems) using the CORAL software. Four random splits of data by making four sets such as active training, passive training, calibration, and validation sets are assessed. The numerical values of sensitivity, specificity, and accuracy for the validation set of splits 1 to 4 are in the range of 0.7726–0.7901, 0.9007–0.9220, and 0.8548–0.8730 respectively. The Matthews correlation coefficient (MCC) of these models for external validation sets is found to be in the range of 0.6832 to 0.7215.