MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
Yiheng Zhu Zhenqiu Ouyang Ben Liao Jialu Wu Yixuan Wu Chang-Yu Hsieh Tingjun Hou Jian Wu
Molecular de novo design is a critical yet challenging task in scientific
fields, aiming to design novel molecular structures with desired property
profiles. Significant progress has been made by resorting to generative models
for graphs. However, limited attention is paid to hierarchical generative
models, which can exploit the inherent hierarchical structure (with rich
semantic information) of the molecular graphs and generate complex molecules of
larger size that we shall demonstrate to be difficult for most existing models.
The primary challenge to hierarchical generation is the non-differentiable
issue caused by the generation of intermediate discrete coarsened graph
structures. To sidestep this issue, we cast the tricky hierarchical generation
problem over discrete spaces as the reverse process of hierarchical
representation learning and propose MolHF, a new hierarchical flow-based model
that generates molecular graphs in a coarse-to-fine manner. Specifically, MolHF
first generates bonds through a multi-scale architecture, then generates atoms
based on the coarsened graph structure at each scale. We demonstrate that MolHF
achieves state-of-the-art performance in random generation and property
optimization, implying its high capacity to model data distribution.
Furthermore, MolHF is the first flow-based model that can be applied to model
larger molecules (polymer) with more than 100 heavy atoms. The code and models
are available at https://github.com/violet-sto/MolHF.