以知识为导向的扩散模型用于3D联体-药对象映射
Jun-Lin Yu1, Cong Zhou1, Xiang-Li Ning1
1Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China.
Nature communications
|March 6, 2025
概括
DiffPhore是一个新的AI框架,通过准确预测3D连接体-药理相映射来增强药物发现. 这种方法可以改善虚拟查,并识别新药候选药物,在药物研究中推进人工智能.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 制药类型的建模.
背景情况:
- 药理对药物发现至关重要,但深度学习的整合仍然有限.
- 现有的工具往往难以准确地绘制3D连接体-药理相的映射.
- 推进人工智能驱动的药方法对于有效的药物开发至关重要.
研究的目的:
- 推出 DiffPhore,一个以知识为导向的扩散框架,用于"随时"的3D连接物-药理相映射.
- 为了提高预测连接体结合形状的准确性和效率.
- 加强虚拟选能力,以发现和目标捕捞.
主要方法:
- 开发了一个以知识为导向的扩散框架 (DiffPhore) 用于配体 - 药相映射.
- 利用了联体-药匹配知识来指导联体构造的生成.
- 采用校准采样,以解决代性形状搜索中的暴露偏差.
- 在两个定制数据集上训练了模型,这些数据集是3D连接体-药对.
主要成果:
- 在预测连接体结合形状方面,DiffPhore取得了最先进的性能.
- 性能优于传统的药工具和先进的对接方法.
- 证明了卓越的虚拟选能力,用于发现和目标捕捞.
- 成功确定了具有验证的结合模式的人类谷氨基基环酶的新型抑制剂.
结论:
- DiffPhore代表了人工智能支持的药导向药物发现的重大进步.
- 该框架为识别潜在的候选药物提供了更高的准确性和效率.
- 这项工作为更广泛地采用基于药理的药物设计中的深度学习铺平了道路.
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