提出自适应去噪调度，加速分子构象生成
Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
- 异步去噪调度捕捉分子层级结构
- 在QM9和GEOM数据集上达到领先性能
- 适合药物设计与分子生成研究者
近期3D分子生成方法主要采用异步自回归或同步扩散模型。自回归模型按序构建分子，但存在视野短和训练推理不一致的问题；同步扩散模型同时去噪所有原子，虽有分子级视野，却难以捕捉层级结构中的因果关系。本文提出等变异步扩散（EAD）模型，结合两者优势：通过异步去噪调度更好建模分子层次结构，同时保持分子级视野。针对复杂依赖关系，设计动态调度机制自适应确定去噪时间步。实验表明，EAD在QM9和GEOM数据集上均达到当前最优性能。
原文摘要 · Abstract (English)
Recent 3D molecular generation methods primarily use asynchronous auto-regressive or synchronous diffusion models. While auto-regressive models build molecules sequentially, they're limited by a short horizon and a discrepancy between training and inference. Conversely, synchronous diffusion models denoise all atoms at once, offering a molecule-level horizon but failing to capture the causal relationships inherent in hierarchical molecular structures. We introduce Equivariant Asynchronous Diffusion (EAD) to overcome these limitations. EAD is a novel diffusion model that combines the strengths of both approaches: it uses an asynchronous denoising schedule to better capture molecular hierarchy while maintaining a molecule-level horizon. Since these relationships are often complex, we propose a dynamic scheduling mechanism to adaptively determine the denoising timestep. Experimental results show that EAD achieves state-of-the-art performance in 3D molecular generation.
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