  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.
