Developing an effective molecular generation framework even with a limited
number of molecules is often important for its practical deployment, e.g., drug
discovery, since acquiring task-related molecular data requires expensive and
time-consuming experimental costs. To tackle this issue, we introduce
Hierarchical textual Inversion for Molecular generation (HI-Mol), a novel
data-efficient molecular generation method. HI-Mol is inspired by the
importance of hierarchical information, e.g., both coarse- and fine-grained
features, in understanding the molecule distribution. We propose to use
multi-level embeddings to reflect such hierarchical features based on the
adoption of the recent textual inversion technique in the visual domain, which
achieves data-efficient image generation. Compared to the conventional textual
inversion method in the image domain using a single-level token embedding, our
multi-level token embeddings allow the model to effectively learn the
underlying low-shot molecule distribution. We then generate molecules based on
the interpolation of the multi-level token embeddings. Extensive experiments
demonstrate the superiority of HI-Mol with notable data-efficiency. For
instance, on QM9, HI-Mol outperforms the prior state-of-the-art method with 50x
less training data. We also show the effectiveness of molecules generated by
HI-Mol in low-shot molecular property prediction.

提出了一种新的数据高效分子生成方法 —— 分层文本反转分子生成（HI-Mol），通过多级嵌入来反应分子分布的多层次特征，进而在少量训练数据的情况下生成高质量的分子。