Implementation of Ensemble Methods on Classification of CDK2 Inhibitor as Anti-Cancer Agent

Abstract

Cancer is known as the second leading cause of death worldwide. About 7-10 million cases of death by cancer occur every year. The recent treatment to heal the cancer is chemotherapy. However, chemotherapy treatment is known to have side effects and cell resistance issues to certain drugs. Therefore, it is required to develop a new drug that can reduce the side effects and provide a better treatment effect. In general, anti-cancer drugs are developed by targeting Cyclin-Dependent Kinase 2 (CDK2) enzyme. Conventional drug design is not effective and efficient for obtaining new drug candidates because of no information about the biological activity before it is synthesized. In this study, we aim to develop a model to predict the activity of CDK2 inhibitors by using ensemble methods, i.e.,  XGBoost, Random Forest, and AdaBoost. The study was conducted by calculating several fingerprints, i.e., Estate, Extended, Maccs, and Pubchem, as feature variables. Based on the results, we found that Random Forest with Pubchem fingerprint gives the best result with the value of Matthews Correlation Coefficient (MCC) and Area Under the ROC Curve (AUC) values are 0.979 and 0.999, respectively. From this study, we contributed to revealing the potency of the ensemble with fingerprint in bioactivity prediction, especially CDK2 inhibitors as anti-cancer agents.