Exploring Computational Methods to Identify Inhibitors for CDK2 Kinase

Abstract Deregulation of Cyclin-Dependent kinase 2 is one of the main causes of lung cancer and is considered as a potential target for cancer therapy. Discovery of the inhibitors against the kinase using traditional methods is costly and time consuming. This issue can be easily dealt with by the usage of potential computational methods. Molecular Dynamic simulation has the ability to offer scientists an in-depth understanding of the dynamics of biomolecules at an atomic level. In the present study the molecular dynamic simulation of Cyclin Dependent kinase is carried out for one microsecond. T-sne and Isomap dimensionality reduction techniques are used followed by different clustering algorithms; Hierarchical, K-means, PAM, Hierarchical K-means and Clara. Subsequently, an optimal number of clusters was generated by using the validation index; Average Silhouette Width (ASW), Dunn Index and Davies Bouldin Index. Further, the cluster representative for each cluster is virtually screened with 52,000 drug-like molecules taken from May bridge library. Based on docking score the top 6 drugs like molecules are selected. Finally, the ADMET properties of the top compounds are evaluated using the Swiss ADMET tool. The strong binding energies and ADMET properties suggest that these molecules are considered to be valuable inhibitors and play a vital role in drug discovery.