Diffusion models have seen wide adoption for 3D molecular generation, yet they offer no principled signal of when a generated molecule is likely to be of low quality. We propose a post-hoc method for estimating per-sample uncertainty in pretrained molecular diffusion models. Building on a Laplace approximation of the denoising network, we measure the variability of the noise prediction across the generation trajectory. Empirically, we show that the resulting uncertainty score is informative of sample quality, exhibiting a negative correlation with established sample-level quality metrics. We further study how the proposed uncertainty score can be used to filter generated samples, improving model performance via test-time scaling.

翻译：扩散模型已被广泛应用于三维分子生成任务，但其无法提供关于生成分子质量高低的原则性信号。我们提出了一种用于预训练分子扩散模型中样本级不确定性估计的事后方法。基于去噪网络的拉普拉斯近似，我们测量了生成轨迹中噪声预测的可变性。实验表明，所得不确定性分数能够有效反映样本质量，与现有样本级质量指标呈负相关关系。我们进一步研究了如何利用所提出的不确定性分数对生成样本进行筛选，通过测试时缩放提升模型性能。