FlowDock:用于生成性蛋白-连接体对接和亲和力预测的几何流量匹配
Alex Morehead1, Jianlin Cheng1
1Department of Electrical Engineering & Computer Science, NextGen Precision Health, University of Missouri-Columbia, Columbia, MO 65211, United States.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
FlowDock 是一种用于药物发现的新型人工智能模型,可以准确预测蛋白质 - 连接体结构和结合亲和关系. 它在对接和虚拟选方面性能优于现有的方法,可以更快地识别潜在的候选药物.
科学领域:
- 计算生物学 计算生物学
- 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:
- 对于蛋白质连接体结构的现有生成AI模型往往缺乏对灵活对接,亲和度估计和并发多连接体建模的支持.
- 对药理学相关目标的严格基准测试是有限的,阻碍了药物发现中的采用.
研究的目的:
- 介绍FlowDock,一个新的深度几何生成模型,用于蛋白质 - 连接体结构预测.
- 为了实现灵活的蛋白质 - 连接体对接和对多个连接体同时进行亲和度估计.
- 通过虚拟查提供一个强大的基准工具,以加速药物发现.
主要方法:
- 使用条件流量匹配 (CFM) 来学习直接映射从未绑定 (apo) 到绑定 (holo) 蛋白质结构.
- 产生具有预测的信心评分和结合亲和值的蛋白质连接体复杂结构.
- 采用深度几何生成建模用于结构预测.
主要成果:
- 在PoseBusters基准测试中,FlowDock实现了51%的盲目对接成功率,仅使用未结合的蛋白质输入,其表现优于单序AlphaFold 3 (AF3).
- 在DockGen-E数据集上展示了强大的绑定口袋泛化,匹配单个序列Chai-1.
- 在第16次结构预测技术的批判性评估 (CASP16) 中排名为结合亲和度估计的前5种方法之一.
结论:
- FlowDock代表了药物发现的生成人工智能的重大进步,提供了准确和高效的蛋白质-连接体复杂模型.
- 它能够处理多个连接体并提供亲和性预测的能力使其成为虚拟查和目标识别的宝贵工具.
- 该模型在多个基准上的表现验证了其在加速新疗法发现方面的潜力.
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