GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Tokyo Institute of Technology（东京工业大学）；Preferred Networks, Inc.（Preferred Networks公司）
英文摘要：
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and (ii) node attributes. This decomposition yields the exact likelihood maximization on graph-structured data, combined with two novel reversible flows. We empirically demonstrate that our model efficiently generates valid molecular graphs with almost no duplicated molecules. In addition, we observe that the learned latent space can be used to generate molecules with desired chemical properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
