药物发现
工作流程
计算机科学
生成语法
数据科学
药品
化学信息学
数据挖掘
化学
人工智能
药理学
医学
数据库
生物化学
计算化学
作者
Lingling Shen,Jian Jun Fang,Lulu Liu,Fengtang Yang,Jeremy L. Jenkins,Peter S. Kutchukian,He Wang
标识
DOI:10.1186/s13321-024-00829-w
摘要
Abstract We present a user-friendly molecular generative pipeline called Pocket Crafter, specifically designed to facilitate hit finding activity in the drug discovery process. This workflow utilized a three-dimensional (3D) generative modeling method Pocket2Mol, for the de novo design of molecules in spatial perspective for the targeted protein structures, followed by filters for chemical-physical properties and drug-likeness, structure–activity relationship analysis, and clustering to generate top virtual hit scaffolds. In our WDR5 case study, we acquired a focused set of 2029 compounds after a targeted searching within Novartis archived library based on the virtual scaffolds. Subsequently, we experimentally profiled these compounds, resulting in a novel chemical scaffold series that demonstrated activity in biochemical and biophysical assays. Pocket Crafter successfully prototyped an effective end-to-end 3D generative chemistry-based workflow for the exploration of new chemical scaffolds, which represents a promising approach in early drug discovery for hit identification.
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