Spanning Tree-based Graph Generation for MoleculesDownload PDF

Published: 28 Jan 2022, Last Modified: 13 Feb 2023ICLR 2022 SpotlightReaders: Everyone
Keywords: molecule generation, tree generation, graph generation, deep generative model, de novo drug design
Abstract: In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry. To this end, we propose a spanning tree-based graph generation (STGG) framework based on formulating molecular graph generation as a construction of a spanning tree and the residual edges. Such a formulation exploits the sparsity of molecular graphs and allows using compact tree-constructive operations to define the molecular graph connectivity. Based on the intermediate graph structure of the construction process, our framework can constrain its generation to molecular graphs that satisfy the chemical valence rules. We also newly design a Transformer architecture with tree-based relative positional encodings for realizing the tree construction procedure. Experiments on QM9, ZINC250k, and MOSES benchmarks verify the effectiveness of the proposed framework in metrics such as validity, Frechet ChemNet distance, and fragment similarity. We also demonstrate the usefulness of STGG in maximizing penalized LogP value of molecules.
One-sentence Summary: We propose a new molecular graph generative model based on compact tree constructive operators.
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