期刊：Journal of Chemical Information and Modeling?2023, 63, 16, 5341–5355
作者：Lin Wei, Min Xu, Zhiqiang Liu, Chongguo Jiang, Xiaohua Lin, Yaogang Hu, Xiaoming Wen, Rongfeng Zou, Chunwang Peng, Hongrui Lin, Guo Wang, Lijun Yang, Lei Fang*, Mingjun Yang, Peiyu Zhang
時(shí)間：2023.08.07
Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the?ID4Inno?drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50?is ～0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in?ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of?ID4inno?in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.
