Classification of CDK2 Inhibitor as Anti-Cancer Agent by Using Simulated Annealing-Support Vector Machine Methods

Abstract

Cancer is a disease that occurs when normal cells divide uncontrollably and attack healthy tissue. This disease is one of the leading causes of death worldwide. There are 10 million cases of cancer deaths based on data from the World Health Organization (WHO). Chemotherapy as a cancer treatment began in 1940 and has been successful since its inception. However, this treatment can be bad for the body in the long term. So, new drug designs are needed to overcome these impacts. Generally, anti-cancer drugs can be developed by considering Cyclin-Dependent Kinases 2 (CDK2) as the target. In designing a new drug, one method that can be used to accelerate the process is the quantitative structure-activity relationships (QSAR) method. This study aims to build a QSAR model for classifying anti-cancer agents from CDK2 inhibitors by using the simulated annealing (SA) and support vector machine (SVM) method. The SA method was used for feature selection, while SVM was used for the model prediction. We utilized the data set used that obtained from the ChemBL database with a total of 1.554 samples. Based on the results, we found that the best prediction model is obtained from SVM with linear and polynomial kernels with accuracy and F-1 score are 0.986 and 0.987, respectively.