The efficacy of diffusion models in generating a spectrum of data modalities, including images, text, and videos, has spurred inquiries into their utility in molecular generation, yielding significant advancements in the field. However, the molecular generation process with diffusion models involves multiple autoregressive steps over a finite time horizon, leading to exposure bias issues inherently. To address the exposure bias issue, we propose a training framework named GapDiff. The core idea of GapDiff is to utilize model-predicted conformations as ground truth probabilistically during training, aiming to mitigate the data distributional disparity between training and inference, thereby enhancing the affinity of generated molecules. We conduct experiments using a 3D molecular generation model on the CrossDocked2020 dataset, and the vina energy and diversity demonstrate the potency of our framework with superior affinity. GapDiff is available at \url{https://github.com/HUGHNew/gapdiff}.

翻译：扩散模型在生成图像、文本和视频等多种数据模态方面的有效性，引发了对其在分子生成中实用性的探究，并推动了该领域的显著进展。然而，基于扩散模型的分子生成过程涉及有限时间范围内的多个自回归步骤，这本质上导致了曝光偏差问题。为解决曝光偏差问题，我们提出了一个名为GapDiff的训练框架。GapDiff的核心思想是在训练过程中以概率方式利用模型预测的构象作为真实值，旨在减少训练与推断之间的数据分布差异，从而提升生成分子的亲和力。我们在CrossDocked2020数据集上使用三维分子生成模型进行了实验，vina能量和多样性指标证明了我们框架在提升亲和力方面的有效性。GapDiff可通过 \url{https://github.com/HUGHNew/gapdiff} 获取。