一个有效的基于碎片的双条件扩散框架,用于分子生成
Haotian Chen1,2,3, Yiting Shen4, Jichun Li5
1Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China.
Briefings in bioinformatics
|January 19, 2026
概括
基于碎片的双条件扩散 (FDC-Diff) 通过生成有效分子来推进基于结构的药物设计. 这种新的框架区分了分子支架和R组,以提高化学可信性和3D结构精度.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 基于碎片的分子生成是基于结构的药物设计 (SBDD) 的关键.
- 现有的方法难以平衡3D结构约束和化学可信性.
- 这种限制源于对分子支架和R组进行模糊的处理.
研究的目的:
- 引入一种新的双条件扩散框架,FDC-Diff,用于基于片段的分子生成.
- 整合化学先验和结构线索,以增强分子生成.
- 改善生成具有化学有效性,合成可行性和药理相关性的分子.
主要方法:
- FDC-Diff将分子生成分解为两个阶段:支架构建和R组编制.
- 第一个阶段为全球拓构建了一个空间受限的支架.
- 第二阶段为本地语义和属性改进添加R组,使用精选的反应规则和以物理化学为灵感的改进步骤.
主要成果:
- 在SBDD基准指标上,FDC-Diff取得了最先进的表现.
- 该模型成功地产生了化学有效和空间兼容的分子.
- 与现有方法相比,FDC-Diff显示出更高的药理学相关性.
结论:
- FDC-Diff为SBDD提供了基于碎片的分子生成的重大进展.
- 该框架能够处理不同的脚手架和R组的不同角色,从而增强了分子设计.
- FDC-Diff显示出作为加速药物发现的实用工具的潜力.
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