提出SITA方法，高效生成分子低温构象，避免传统方法的高计算开销。
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
- 用能量模型替代分数场发散，实现可扩展的推理时退火
- 在二肽和三肽系统上达到当前最优采样性能
- 适合需要快速生成低温分子构象的研究者
计算化学与生物物理中长期存在的挑战是如何高效采样分子的玻尔兹曼分布。生成建模的进展通过消除模拟的计算成本，试图克服传统采样技术的局限性。一种有前景的方法是沿温度梯度迭代微调扩散模型，推理时通过重要性采样生成训练数据。然而，这些方法需计算分数场的散度以估计重要性权重，对大系统难以处理。本文提出可扩展的推理时退火（SITA），通过能量模型重训练流模型，在逐步降低温度下生成样本，实现快速代理似然。在丙氨酸二肽和三肽系统上均实现当前最优性能，且避免了昂贵的散度项。代码已公开于 https://github.com/countrsignal/sita.git。
原文摘要 · Abstract (English)
A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational cost of simulation. A promising direction is iteratively finetuning diffusion models along a temperature ladder whereby training data is generated via importance sampling during inference-time annealing. Unfortunately, these methods require computing a divergence over the score field to estimate importance weights, rendering them intractable for larger systems. Here we present scalable inference-time annealing (SITA), which retrains flow-based models to generate samples at progressively lower temperatures using an energy-based model to facilitate fast surrogate likelihoods. We demonstrate state-of-the-art performance on both Alanine Dipeptide and Alanine Tripeptide while avoiding costly divergence terms. Our code is available at https://github.com/countrsignal/sita.git
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