We introduce a new framework for molecular graph generation with 3D molecular
generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps
molecular graphs to Euclidean point clouds via synthetic conformer coordinates
and learns the inverse map using an E(n)-Equivariant Graph Neural Network
(EGNN). The induced point cloud-structured latent space is well-suited to apply
existing 3D molecular generative models. This approach simplifies the graph
generation problem - without relying on molecular fragments nor autoregressive
decoding - into a point cloud generation problem followed by node and edge
classification tasks. Further, we propose a novel similarity-constrained
optimization scheme for 3D diffusion models based on inpainting and guidance.
As a concrete implementation of our framework, we develop EDM-SyCo based on the
E(3) Equivariant Diffusion Model (EDM). EDM-SyCo achieves state-of-the-art
performance in distribution learning of molecular graphs, outperforming the
best non-autoregressive methods by more than 30% on ZINC250K and 16% on the
large-scale GuacaMol dataset while improving conditional generation by up to
3.9 times.

通过合成构象坐标和 E (n)- 等变图神经网络 (EGNN) 将分子图映射到欧几里得点云，我们介绍了一种新的分子图生成的框架 ——SyCo。学习其逆映射后，可以应用现有的 3D 分子生成模型。此方法将图生成问题简化为点云生成问题，随后进行节点和边的分类任务。此外，我们提出了一种基于修复和引导的新型相似性约束的 3D 扩散模型优化方案。作为我们框架的一个具体实现，我们开发了基于 E (3) 等变扩散模型 (EDM) 的 EDM-SyCo，在分子图的分布学习中实现了最先进的性能，在 ZINC250K 和大规模 GuacaMol 数据集上优于最佳的非自回归方法 30% 以上，同时将条件生成的性能提高了最多 3.9 倍。