A Robust Computational Framework for the Optimization of CDK7 Inhibitors as Promising Cancer Therapy

Abstract

Cyclin-dependent kinase 7 (CDK7) plays a crucial role in cell cycle regulation and transcription, establishing it as a promising target for cancer therapy. Although the covalent inhibitor THZ1 effectively targets CDK7, it presents risks such as short half-life and potential off-target side effects. To address these limitations, we employed a computational workflow integrating virtual screening, molecular dynamics (MD) simulations, and free energy perturbation (FEP) method to design non-covalent CDK7 inhibitors with enhanced selectivity and safety profiles. MD simulations elucidated THZ1's inhibitory mechanism and identified key molecular fragments within its structure. By incorporating fragments from known inhibitors, we introduced extensive non-covalent interactions within the binding pocket, leading to the identification of three novel non-covalent inhibitors with binding affinities comparable to or higher than that of THZ1. Our findings not only introduce promising CDK7 inhibitors but also present a robust computational framework that could accelerate the discovery of kinase-targeted therapeutics.