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

翻译：计算化学与生物物理学中一个长期存在的挑战是如何高效地对分子的玻尔兹曼分布进行采样。生成式建模的进步被提出用于克服传统采样技术的局限性，通过消除模拟的计算成本。一个有前景的方向是沿温度阶梯迭代微调扩散模型，其中训练数据通过推断时退火期间的重要性采样生成。不幸的是，这些方法需要计算得分场上的散度来估计重要性权重，使其对于较大系统而言难以处理。在此，我们提出可扩展推断时退火（SITA），该方法在逐步降低温度时重新训练基于流的模型以生成样本，并利用基于能量的模型来促进快速代理似然。我们在丙氨酸二肽和丙氨酸三肽上均展示了最先进的性能，同时避免了昂贵的散度项。我们的代码可在 https://github.com/countrsignal/sita.git 获取。