Designing of target‐specific N‐substituted tetrahydroquinoline derivatives as potent mTOR inhibitors via integrated structure‐guided computational approaches

Abstract There is a need to develop innovative, safe, and more effective treatment methods for lung cancer due to obstacles including resistance, side effects, and low bioavailability, among others related to present medicines. Our previous research findings gave some promising results of tetrahydroquinoline (THQ) derivatives as mTOR inhibitors for the treatment of lung cancer. The current research has employed certain integrated computational techniques to enhance the effectiveness of THQ derivatives. Various computational methods, like the generation of the pharmacophore model, virtual screening, and QSAR modeling were employed to achieve the goal, and their results were connected to develop novel THQ analogues. This study revealed that among 48 newly designed THQ derivatives, the compound UC_1 indicated the XP Gscore of −9.378 kcal/mol and also showed the key amino acid interactions with the mTOR protein viz. Gln2167, Val2240. Interestingly, compound UC_1 showed a better binding affinity with the mTOR protein compared to the clinical trial molecules viz. AZD‐8055 and AZD‐2014. The molecular dynamics simulations (100 ns) along with each output give a shred of strong evidence that the designed THQ derivative (UC_1) has all the required features in terms of inhibiting mTOR protein for the treatment of lung cancer.