GraphDF: A Discrete Flow Model for Molecular Graph Generation

• URL: http://arxiv.org/abs/2102.01189v1
• Date: Mon, 1 Feb 2021 21:39:41 GMT
• Title: GraphDF: A Discrete Flow Model for Molecular Graph Generation
• Authors: Youzhi Luo, Keqiang Yan, Shuiwang Ji
• Abstract summary: GraphDF is a novel discrete latent variable model for molecular graph generation based on normalizing flow methods. We show that the use of discrete latent variables reduces computational costs and eliminates the negative effect of dequantization.
• Score: 39.37343930928477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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