Data-Efficient Molecular Generation with Hierarchical Textual Inversion
<|reference_start|>Data-Efficient Molecular Generation with Hierarchical Textual Inversion: 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.<|reference_end|>
@article{kim2024data-efficient,
    title={Data-Efficient Molecular Generation with Hierarchical Textual Inversion},
    author={Seojin Kim, Jaehyun Nam, Sihyun Yu, Younghoon Shin, Jinwoo Shin},
    journal={arXiv preprint arXiv:2405.02845},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02845},
    primaryClass={cs.LG q-bio.MN}
}