MolHF：用于分子图生成的分层归一化流
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
AI总结：
MolHF提出一种分层归一化流模型，以从粗到细方式生成分子图，解决离散分层生成不可微问题，实现随机生成和性质优化最先进性能，并首次适用于超过100个重原子的大分子。
摘要：
分子从头设计是科学领域中一项关键且具有挑战性的任务，旨在设计具有所需性质特征的新型分子结构。通过借助图生成模型，已经取得了重大进展。然而，对分层生成模型的关注有限，这类模型能够利用分子图固有的分层结构（具有丰富的语义信息），并生成更大尺寸的复杂分子，我们将证明这对大多数现有模型来说是困难的。分层生成的主要挑战是由中间离散粗化图结构的生成引起的不可微问题。为了回避这一问题，我们将离散空间上棘手的分层生成问题视为分层表示学习的逆过程，并提出了MolHF，一种新的基于流的分层模型，以从粗到细的方式生成分子图。具体来说，MolHF首先通过多尺度架构生成键，然后基于每个尺度上的粗化图结构生成原子。我们证明，MolHF在随机生成和性质优化方面达到了最先进的性能，表明其建模数据分布的高能力。此外，MolHF是第一个能够应用于建模具有超过100个重原子的大分子（聚合物）的基于流的模型。代码和模型可在https://github.com/violet-sto/MolHF获取。
英文摘要：
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.
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