MoFlow: An Invertible Flow Model for Generating Molecular Graphs

• URL: http://arxiv.org/abs/2006.10137v1
• Date: Wed, 17 Jun 2020 20:14:19 GMT
• Title: MoFlow: An Invertible Flow Model for Generating Molecular Graphs
• Authors: Chengxi Zang and Fei Wang
• Abstract summary: MoFlow is a flow-based graph generative model to learn invertible mappings between molecular graphs and latent representations. Our model has merits including exact and tractable likelihood training, efficient one-pass embedding and generation, chemical validity guarantees, 100% reconstruction of training data, and good generalization ability.
• Score: 19.829612234339578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process. Such graph generative models usually consist of two steps: learning latent representations and generation of molecular graphs. However, to generate novel and chemically-valid molecular graphs from latent representations is very challenging because of the chemical constraints and combinatorial complexity of molecular graphs. In this paper, we propose MoFlow, a flow-based graph generative model to learn invertible mappings between molecular graphs and their latent representations. To generate molecular graphs, our MoFlow first generates bonds (edges) through a Glow based model, then generates atoms (nodes) given bonds by a novel graph conditional flow, and finally assembles them into a chemically valid molecular graph with a posthoc validity correction. Our MoFlow has merits including exact and tractable likelihood training, efficient one-pass embedding and generation, chemical validity guarantees, 100\% reconstruction of training data, and good generalization ability. We validate our model by four tasks: molecular graph generation and reconstruction, visualization of the continuous latent space, property optimization, and constrained property optimization. Our MoFlow achieves state-of-the-art performance, which implies its potential efficiency and effectiveness to explore large chemical space for drug discovery.

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