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。

GraphAF: 分子图生成的基于流的自回归模型