Discovery of novel CDK2 inhibitors for cancer treatment: integrating ligand-based pharmacophore modelling, molecular docking, DFT, ADMET, and molecular dynamics simulation studies

Background

The global landscape of public health faces significant challenges attributed to the prevalence of cancer and the emergence of treatment resistance. This study addresses these challenges by focusing on Cyclin-dependent Kinase 2 (CDK2) and employing a systematic computational approach for the discovery of novel cancer therapeutics.

Results

Initial ligand-based pharmacophore modelling, utilizing a training set of five reported CDK2 inhibitors, yielded a robust model characterized by Aro|Hyd| and |Acc|Don| features. Screening this validated model against the ZINC database identified 1881 hits, which were further subjected to molecular docking studies. The top 10 compounds (Z1–Z10) selected from the docking studies underwent Pharmacokinetic parameters Absorption, Distribution, Metabolism, Excretion and Toxicity profiling, Density Functional Theory (DFT) studies and the top two went for 100ns molecular dynamics (MD) simulations by comparing them with the standard Roscovitine. Compounds Z1 and Z2 emerged as the most promising, with docking scores of − 8.05 kcal/mol and − 8.02 kcal/mol, respectively. DFT analysis of the top 10 compounds revealed minimal variations in highest occupied molecular orbital–lowest unoccupied molecular orbital energy gaps, indicating consistent electronic stability and reactivity across the candidates. MD simulations of Z1 and Z2 confirmed their stable interactions with CDK2, with root mean square deviation (RMSD) values ranging from 1.4 to 2.5 Å for Z1 and 1.5 to 2.4 Å for Z2.

Conclusion

The current research identified compounds Z1 and Z2, which demonstrated significant potential as potent CDK2 inhibitors for cancer therapy, providing valuable insights into the development of more effective CDK2 inhibitors and addressing the critical need for innovative therapeutic strategies in cancer treatment.

Graphical abstract