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。

提升您的分子：在潜在欧几里得空间中生成分子图

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean  Space

We consider the problem of molecular graph generation using deep models.
While graphs are discrete, most existing methods use continuous latent
variables, resulting in inaccurate modeling of discrete graph structures. In
this work, we propose GraphDF, a novel discrete latent variable model for
molecular graph generation based on normalizing flow methods. GraphDF uses
invertible modulo shift transforms to map discrete latent variables to graph
nodes and edges. We show that the use of discrete latent variables reduces
computational costs and eliminates the negative effect of dequantization.
Comprehensive experimental results show that GraphDF outperforms prior methods
on random generation, property optimization, and constrained optimization
tasks.

本研究中，我们提出了一种名为 GraphDF 的离散潜变量模型，使用归一化流方法解决了利用深度模型生成分子图时因离散图结构建模的不准确性而导致的连续潜变量的问题。我们使用可逆的模数转换将离散潜变量映射为图节点和边，实验证明该方法在随机生成、属性优化和约束优化任务中优于先前方法。

GraphDF: 分子图生成的离散流模型

GraphDF: A Discrete Flow Model for Molecular Graph Generation

Molecular graph generation is a fundamental problem for drug discovery and
has been attracting growing attention. The problem is challenging since it
requires not only generating chemically valid molecular structures but also
optimizing their chemical properties in the meantime. Inspired by the recent
progress in deep generative models, in this paper we propose a flow-based
autoregressive model for graph generation called GraphAF. GraphAF combines the
advantages of both autoregressive and flow-based approaches and enjoys: (1)
high model flexibility for data density estimation; (2) efficient parallel
computation for training; (3) an iterative sampling process, which allows
leveraging chemical domain knowledge for valency checking. Experimental results
show that GraphAF is able to generate 68% chemically valid molecules even
without chemical knowledge rules and 100% valid molecules with chemical rules.
The training process of GraphAF is two times faster than the existing
state-of-the-art approach GCPN. After fine-tuning the model for goal-directed
property optimization with reinforcement learning, GraphAF achieves
state-of-the-art performance on both chemical property optimization and
constrained property optimization.

本文提出了一种基于流模型的自回归图生成模型，即 GraphAF。GraphAF 结合了自回归和基于流的方法优点，具有高模型灵活性、高效的并行计算以及迭代采样过程，能够在无化学规则的情况下生成 68% 的化学有效分子，并能利用化学领域知识进行价态检查。在应用强化学习进行目标性能优化的 fine-tuning 后，GraphAF 实现了化学性质优化和受约束性能优化两方面的最佳表现。