A hybrid energy-based and AI-based screening approach for the discovery of novel inhibitors of JAK3

Frontiers in Medicine(2023)

引用 0|浏览6
暂无评分
摘要
The JAKs protein family is composed 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 a novel sketch from a specific “In-house” database. Using 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 the MOLM-16 cell line, providing a 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 a novel sketch possessing inhibitory bioactivity against JAK3, and it would provide an acceptable “hit” for further structure optimization and modification to develop JAK3 inhibitors.
更多
查看译文
关键词
JAK3,virtual screening,bioactivity evaluation,molecular dynamics,geometric deep learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要