用潜在空间扩散模型生成3D分子，兼顾多样性和几何准确性。
LMDM:Latent Molecular Diffusion Model For 3D Molecule Generation
- 在潜在空间建模原子间力与局部约束，保持几何等变性。
- 相比传统方法，收敛更快，生成样本质量显著提升。
- 适合药物分子设计、生成化学多样性分子的场景。
本文提出一种潜在分子扩散模型（LMDM），可在潜在空间中捕捉原子间的力和局部约束，生成具有丰富多样性和精确几何特征的3D分子。通过利用潜在变量的低秩流形特性融合原子间作用力信息，有效维持分子的几何等变性。无需分阶段的信息融合编码，降低了反向传播计算量。模型在潜在空间中保留粒子键的力与约束，缓解了网络表面欠拟合导致的粒子位置漂移问题，从而实现更早收敛。每一步反向过程中引入分布控制变量，增强探索能力，进一步提升生成多样性。实验表明，生成样本质量与模型收敛速度均显著优于传统方法。
原文摘要 · Abstract (English)
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.
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