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

Rahat Shahrior, Salwa Tamkin, Mohammad Badhruddouza Khan, Ahmed Jebail Meraj, Hanif Bhuiyan

Abstract: Highlights•Overexpression of AKT2 is linked to metastasis in colorectal, ovarian & breast cancer.•Classifying active and inactive AKT2 inhibitors using eight machine learning models.•Docking study revealed binding affinities ranging from −10.9 to −9.8kcal/mol at most.•Three lead compounds were selected with excellent ADMET profiles.•100 ns MD simulation ensured eminent binding stability; comparable to Capivasertib.