用层次一致性扩散模型生成用于蛋白结合的分子
Guanlue Li1, Chenran Jiang2, Ziqi Gao3
1Data Science and Analytics, The Hong Kong University of Science and Technology (Guang Zhou) Guangzhou 511400 China guanlueli@gmail.com.
Chemical science
|September 11, 2025
概括
我们开发了一种新的AI模型,即原子动图一致性扩散模型 (AMDiff),用于生成有效和新型药物分子. 这种方法增强了针对特定蛋白质点的基于结构的药物设计.
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
- 分子建模分子建模
背景情况:
- 产生有效和可靠的3D分子结构对于药物发现至关重要.
- 当前的原子和动机智能模型面临着分子有效性和可靠性的挑战.
- 弥合原子视图和动机视图的方法是必要的,以实现全面的分子生成.
研究的目的:
- 开发一种先进的AI模型,用于有效的3D分子结构生成.
- 为了提高药物发现产生的分子的有效性和可靠性.
- 加快针对特定目标的候选药物的设计.
主要方法:
- 开发了原子-动图一致性扩散模型 (AMDiff),使用多视图学习的联合培训范式.
- 实现了分层扩散架构,集成了原子和动图视图.
- 使用无分类器指导和拓特征作为条件输入.
主要成果:
- 与现有方法相比,AMDiff在生成不同蛋白质口袋的分子方面表现出优越的有效性和新性.
- 该模型成功生成了适合特定蛋白质标的分子.
- 对无细胞性淋巴瘤激酶 (ALK) 和循环素依赖激酶4 (CDK4) 的案例研究表明,在基于结构的新药设计中具有有效性.
结论:
- AMDiff有效地整合了原子和动图级别的信息,以实现强大的分子生成.
- 该模型通过提高分子有效性和新性来增强基于结构的新药设计.
- 在药物发现中,AMDiff加速了针对特定目标分子的开发.
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