QSAR Classification-Based Virtual Screening Followed by Molecular Docking Identification of Potential COX-2 Inhibitors in a Natural Product Library.

Abstract

Developments of natural inhibitors to prevent the function of cyclooxygenase-2 (COX-2) protein, responsible for a variety of inflammations and cancers, are a major challenge in the scientific community. In this study, robust QSAR classification models for predicting COX-2 inhibitor were developed, by which the self-organizing feature map neural network and random forest (RF) were adopted to improve the prediction of classification model ability. The F-score-based criterion combined with RF was used for feature selection, and good performance for COX-2 inhibitor prediction in overall accuracy was demonstrated. We used this model as a virtual screening tool for identifying the potential COX-2 inhibitor from a natural product library and found potential hit compounds. This compound further screened by applying molecular docking simulation identified five potential hits such as osthole, kavain, vanillyl acetone, myristicin, and psoralen, having a comparable binding affinity to COX-2 protein. However, in cell experiment, three hit compounds revealed COX-2 inhibitory activity in mRNA and protein level such as osthole, kavain, and psoralen.