A hybrid energy-based and AI-based screening approach for the discovery of novel inhibitors of JAK3 - Wei, Juying; Pan, Youlu; Shen, Zheyuan; Shen, Liteng; Xu, Lei; Yu, Wenjuan; Huang, Wenhai (Frontiers in Medicine, 2023)
The JAKs protein family are comprised of four isoforms, and JAK3 has been regarded as a druggable target for the development of drugs to treat various diseases, including hematologic tumors, cancer, and neuronal death. Therefore, the discovery of JAK3 inhibitors with novel scaffolds possesses the potential to provide additional options for drug development. This article presents a structure-based hybrid high-throughput virtual screening (HTVS) protocol as well as the DeepDock algorithm, which is based on geometric deep learning. These techniques were used to identify inhibitors of JAK3 with novel sketch from a specific "In-house" database. Via molecular docking with varying precision, MM/GBSA, geometric deep learning scoring and manual selection, 10 compounds were obtained for subsequent biological evaluation. One of these 10 compounds, compound 8, was found to have inhibitory potency against JAK3 and MOLM-16 cell line, providing valuable lead compound for further development of JAK3 inhibitors. To gain a better understanding of the interaction between compound 8 and JAK3, molecular dynamics (MD) simulations were conducted to provide more details on the binding conformation of compound 8 with JAK3 to guide the subsequent structure optimization. In this article, we achieved compound 8 with novel sketch possessing inhibitory bioactivity against JAK3, and it would provide an acceptable "hit" for the further structure optimization and modification to develop JAK3 inhibitors.