做分子生成或药物设计的团队，终于有了一个能判断生成分子质量好坏的信号，建议试试这个后处理方法，能直接提升模型输出质量。
扩散模型在3D分子生成中广泛应用，但缺乏对生成分子质量的可靠信号。研究者提出一种后处理方法，基于拉普拉斯近似对预训练分子扩散模型进行逐样本不确定性估计。该方法通过测量生成轨迹中噪声预测的变异性来评估不确定性，实验表明该分数与样本质量负相关。利用该分数过滤生成样本，可在测试时提升模型性能。
Uncertainty Estimation for Molecular Diffusion Models
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.