Abstract:
Kinases are one of the many enzymes that are absolutely essential for the regulation of
cell division and in the performance of so many actions in the cell. They are implicated
with many diseases, most notably cancer, which underlines the fact that they might
be valuable therapeutic objectives. In the present study, we incorporated machine
learning methods alongside molecular docking to determine the binding energies of the
candidate compounds for improving the opportunity of drug discovery. The binding
interactions between a curated set of 369 ligands, primarily protein kinase inhibitors,
and ten selected kinase proteins critical to cancer signaling pathways were investigated.
Ligands were meticulously chosen from the PKIDB database based on their relevance
to anticancer drug development. Protein structures were obtained from the RCSB
Protein Data Bank and prepared for docking using Chimera to ensure biologically
relevant conformations. Active binding sites were identified with DogSitescorer, and
docking was performed using AutoDock Tools and Vina_split, facilitating systematic
analysis of protein-ligand interactions. The binding affinities ranged differently and it
was observed that compounds having hydroxyl groups on benzene rings showed higher
affinities. However, eribulin had the highest binding affinity of 12.4 kcal/mol for erbB2
while Bemcentinib had the following affinities: 11.9 kcal/mol with PLK1 and 12. 13
kcal/mol with RET amongst the multiple kinases. The aforementioned analysis exposed details about interactions like the hydrogen bonds and the Van der Waals which
enabled understanding of the molecular mechanism in binding effectiveness. Specifically, this study focuses on the application of these ligands as potential inhibitors in
therapeutic fields along with helpful guidelines for the kinase targeted anti-cancer drug
design accompanied by machine learning based compound predicting model.
