n this work, we propose a latent molecular diffusion model that can make the generated 3D molecules rich in diversity and maintain rich geometric features. The model captures the information of the forces and local constraints between atoms so that the generated molecules can maintain Euclidean transformation and high level of effectiveness and diversity. We also use the lowerrank manifold advantage of the latent variables of the latent model to fuse the information of the forces between atoms to better maintain the geometric equivariant properties of the molecules. Because there is no need to perform information fusion encoding in stages like traditional encoders and decoders, this reduces the amount of calculation in the back-propagation process. The model keeps the forces and local constraints of particle bonds in the latent variable space, reducing the impact of underfitting on the surface of the network on the large position drift of the particle geometry, so that our model can converge earlier. We introduce a distribution control variable in each backward step to strengthen exploration and improve the diversity of generation. In the experiment, the quality of the samples we generated and the convergence speed of the model have been significantly improved.

翻译：本研究提出了一种潜在分子扩散模型，该模型能够使生成的三维分子具有丰富的多样性并保持充分的几何特征。该模型捕获了原子间作用力与局部约束信息，从而使生成的分子能够保持欧几里得变换不变性，同时具备高度的有效性与多样性。我们利用潜在模型隐变量的低秩流形优势，融合原子间作用力信息，以更好地保持分子的几何等变特性。由于无需像传统编码器-解码器那样进行分阶段的信息融合编码，这减少了反向传播过程中的计算量。该模型将粒子键合作用力与局部约束保持在隐变量空间中，降低了网络表层欠拟合对粒子几何结构大范围位置漂移的影响，从而使我们的模型能够更早收敛。我们在每个反向步骤中引入分布控制变量以增强探索能力，提高生成多样性。实验结果表明，我们生成样本的质量与模型的收敛速度均得到显著提升。