Introduction: The cytosolic Ser/Thr kinase TBK1 is of utmost importance in
facilitating signals that facilitate tumor migration and growth. TBK1-related
signaling plays important role in tumor progression, and there is need to work
on new methods and workflows to identify new molecules for potential
treatments for TBK1-affecting oncologies such as breast cancer.
Methods: Here, we propose the machine learning assisted computational drug
discovery approach to identify TBK1 inhibitors. Through our computational MLintegrated
approach, we identified four novel inhibitors that could be used as new
hit molecules for TBK1 inhibition.
Results and Discussion: All these four molecules displayed solvent based free
energy values of −48.78, −47.56, −46.78 and −45.47 Kcal/mol and glide docking
score of −10.4, −9.84, −10.03, −10.06 Kcal/mol respectively. The molecules
displayed highly stable RMSD plots, hydrogen bond patterns and MMPBSA
score close to or higher than BX795 molecule. In future, all these compounds
can be further refined or validated by in vitro as well as in vivo activity. Also, we
have found two novel groups that have the potential to be utilized in a fragmentbased
design strategy for the discovery and development of novel inhibitors
targeting TBK1. Our method for identifying small molecule inhibitors can be used
to make fundamental advances in drug design methods for the TBK1 protein
which will further help to reduce breast cancer incidence.