药物发现
生成语法
深度学习
计算机科学
生成模型
脚手架
虚拟筛选
坏死性下垂
生成对抗网络
人工智能
药物重新定位
计算生物学
药品
生物信息学
化学
生物
程序性细胞死亡
药理学
生物化学
数据库
细胞凋亡
作者
Yueshan Li,Liting Zhang,Yifei Wang,Jun Zou,Ruicheng Yang,Xinling Luo,Chengyong Wu,Wei Yang,Chenyu Tian,Haixing Xu,Falu Wang,Xin Yang,Linli Li,Shengyong Yang
标识
DOI:10.1038/s41467-022-34692-w
摘要
The retrieval of hit/lead compounds with novel scaffolds during early drug development is an important but challenging task. Various generative models have been proposed to create drug-like molecules. However, the capacity of these generative models to design wet-lab-validated and target-specific molecules with novel scaffolds has hardly been verified. We herein propose a generative deep learning (GDL) model, a distribution-learning conditional recurrent neural network (cRNN), to generate tailor-made virtual compound libraries for given biological targets. The GDL model is then applied to RIPK1. Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selective RIPK1 inhibitor with a previously unreported scaffold, RI-962. This compound displays potent in vitro activity in protecting cells from necroptosis, and good in vivo efficacy in two inflammatory models. Collectively, the findings prove the capacity of our GDL model in generating hit/lead compounds with unreported scaffolds, highlighting a great potential of deep learning in drug discovery.
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