深度退化:一个结构意识的深度学习框架,用于PROTAC和对蛋白质标的配体生成
Qiaoyu Hu1, Yu Cao1,2, PengXuan Ren1,3
1Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai 201210, China.
概括
一个人工智能工具DeepDegradome自动化了蛋白解向金马 (PROTAC) 和小分子的设计. 这种方法通过为具有挑战性的蛋白质标创建新的分子架构来增强药物发现.
科学领域:
- 药用化学 医学化学
- 人工智能的人工智能
- 药物发现 药物发现 药物发现
背景情况:
- 使用蛋白质溶解向金马 (PROTACs) 的向蛋白质降解是药物发现的关键策略.
- 设计有效的PROTAC具有挑战性,特别是对于缺乏明确结合位点的蛋白质,目前的方法受到固定连接体和链接器的限制.
研究的目的:
- 介绍DeepDegradome,这是一个由人工智能驱动的平台,用于小型分子配体和PROTACs的自动化,结构意识的设计.
- 通过使联体和PROTACs的新构造成为可能,克服当前PROTAC设计方法的局限性.
主要方法:
- 利用来自公共数据库的大片段库和内部对接方法 (iFitDock) 进行初始片段识别.
- 根据目标蛋白质口袋特征组装碎片,以构建新型连接体.
- 从生成的连接体构建PROTAC,独立于预定义的弹头或E3连接体.
主要成果:
- 与其他AI模型相比,DeepDegradome产生了更有效的,类似药物的分子,预测的结合亲和力更高.
- 成功设计和验证了针对WDR5和CDK9标的强效抑制剂和PROTAC.
- 合成的化合物显示出通过X射线晶体学预测和实验确定的结合形状之间的高度一致.
结论:
- DeepDegradome提供了一个可扩展和可靠的AI驱动的解决方案,用于设计连接体和PROTAC.
- 该平台通过为具有挑战性的蛋白质标提供创新分子设计,促进新药的发现.
- PROTACs和配体的自动设计加速了药物发现管道.
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