MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation

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.

Authors

• Yiheng Zhu College of Computer Science & Technology and Liangzhu Laboratory, Zhejiang University
• Zhenqiu Ouyang
• Ben Liao
• Jialu Wu College of Pharmaceutical Sciences, Zhejiang University
• Yixuan Wu Zhejiang University
• Chang-Yu Hsieh College of Pharmaceutical Sciences, Zhejiang University
• Tingjun Hou College of Pharmaceutical Sciences, Zhejiang University
• Jian Wu State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital, Zhejiang University Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence

Keywords

• Machine Learning: ML: Probabilistic machine learning
• Machine Learning: ML: Sequence and graph learning
• Multidisciplinary Topics and Applications: MDA: Health and medicine