In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis

Background and Objective: In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in colorectal cancer and its metastasis. Again, exceeding genomic arrangements of AKT2 can be held responsible for HGSC (High-grade serous ovarian cancer) and breast cancer cell metastasis. FDA-approved capivasertib, a potential drug targeting the AKT signaling pathway, has a few side effects such as plausible alterations of liver function and gastrointestinal issues. Hence, this research aims to detect compounds with higher drug potency for selective AKT2 inhibition to encounter the incidence of different types of cancer cell metastasis. Methods: Eight machine-learning models were engaged to classify active and inactive drug candidates among 1148 collected compounds from the CHEMBL database. Potential drug candidates with greater IC₅₀ value and no Lipinski violations were then addressed to molecular docking and molecular dynamics simulation using PyRx, AutoDock Vina and Desmond package. Results: From docking studies, three of the initial drug candidates provided greater binding affinities within a range from -10.9 to -9.8 kcal/mol, comparable to that of Capivasertib and backed up by post-docking MM/GBSA analysis. Again, the prediction of pharmacokinetic properties and bioactivity scores of drug candidates revealed their drug-likeliness and safer ADMET profiles for future clinical trials. Finally, 100 ns MD simulation computation for these lead compounds exhibited greater stability and drug potency during interactions with AKT2 protein, followed by PCA and DCCM analysis. Conclusion: However, future in-vivo research can ascertain whether our proposed drug candidates can pass the standard clinical trials as publicly accessible novel drug targets.