Identification of TBK1 inhibitors against breast cancer using a computational approach supported by machine learning - Siddiqui, Arif Jamal; Jamal, Arshad; Zafar, Mubashir; Jahan, Sadaf (Frontiers in Pharmacology, 2024)
The cytosolic Ser/Thr kinase TBK1 is of utmost importance in facilitating signals that facilitate tumor migration and growth. The signaling pathway associated with TBK1 plays a significant role in the progression of tumors. Consequently, it is imperative to develop novel methodologies and workflows to uncover previously undiscovered compounds that have the potential to be used as therapeutics for TBK1-related oncological conditions, including breast cancer. In this study, we provide a novel computational drug discovery approach that utilizes machine learning techniques to identify inhibitors of TBK1. Using our computational machine learning-integrated methodology, we have successfully found four previously undiscovered inhibitors that exhibit potential as novel hit molecules for inhibiting TBK1. The solvent-based free energy values of the molecules were observed to be -48. 78, -47.56, -46.78, and -45.47 Kcal/mol, while their glide docking scores were -10.4, -9.84, -10.03, and -10.06 Kcal/mol, respectively. The compounds exhibited remarkably stable root-mean-square deviation (RMSD) plots, hydrogen bond patterns, and MMPBSA scores that were comparable to or greater than those of the BX795 molecule. The compounds can undergo additional refinement or optimization through the application of medicinal chemistry techniques, and their efficacy can be confirmed using both in vitro and in vivo activity assays. Additionally, we have found two novel groups that have the potential to be utilized in a fragment-based design strategy for the discovery and development of novel inhibitors targeting TBK1. The application of our methodology for the identification of small molecule inhibitors has potential for significant advancements in drug design strategies targeting the TBK1 protein, hence contributing to the reduction of breast cancer incidence.