用于层级分子构象体生成的等变模糊扩散模型
Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation
AI总结：
本文提出 Equivariant Blurring Diffusion（EBD），以两阶段由粗到细生成分子构象体，并在类药分子基准上验证其优于现有去噪扩散模型。
摘要：
扩散模型如何能够以由粗到细的方式处理三维几何结构，就像我们对世界的多尺度观察一样？本文中，我们通过聚焦于一个基础生物化学问题来回答这一问题：以多尺度方式生成以分子图为条件的三维分子构象体。我们的方法包含两个层级阶段：i）从分子图生成粗粒度的片段级三维结构；ii）从粗粒度近似结构生成精细的原子细节，同时允许对粗粒度结构进行同步调整。第二阶段颇具挑战性，需要在保留粗粒度信息的同时确保 SE(3) 等变性。为此，我们提出了一种名为 Equivariant Blurring Diffusion（EBD，等变模糊扩散）的新型生成模型。该模型定义了一个正向过程，通过模糊构象体的精细原子细节而趋向片段级粗粒度结构；并定义了一个反向过程，利用等变网络执行相反操作。我们在一个类药分子基准上，通过与最先进的去噪扩散模型进行几何和化学比较，证明了 EBD 的有效性。消融研究通过全面分析其架构（包括损失函数和数据破坏过程的设计）得出了关于 EBD 设计的洞见。代码已发布于 https://github.com/Shen-Lab/EBD 。
英文摘要：
How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world? In this paper, we address the question by focusing on a fundamental biochemical problem of generating 3D molecular conformers conditioned on molecular graphs in a multiscale manner. Our approach consists of two hierarchical stages: i) generation of coarse-grained fragment-level 3D structure from the molecular graph, and ii) generation of fine atomic details from the coarse-grained approximated structure while allowing the latter to be adjusted simultaneously. For the challenging second stage, which demands preserving coarse-grained information while ensuring SE(3) equivariance, we introduce a novel generative model termed Equivariant Blurring Diffusion (EBD), which defines a forward process that moves towards the fragment-level coarse-grained structure by blurring the fine atomic details of conformers, and a reverse process that performs the opposite operation using equivariant networks. We demonstrate the effectiveness of EBD by geometric and chemical comparison to state-of-the-art denoising diffusion models on a benchmark of drug-like molecules. Ablation studies draw insights on the design of EBD by thoroughly analyzing its architecture, which includes the design of the loss function and the data corruption process. Codes are released at https://github.com/Shen-Lab/EBD .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
